06_T5_LDA.ipynb

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    "# LDA module\n",
    "In this otebook, we performed our unsupervised learning task - LDA Topic modelling.  \n",
    "\n",
    "The analysis follows a systematic approach of :\n",
    "1. Exploration / Hyperparameter tuning\n",
    "2. Sensitivity analysis - Coherence score / perplexity score\n",
    "3. Topic exploration using pyLDAvis\n"
   ]
  },
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   "cell_type": "code",
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   "id": "eZSemI_PY20G",
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   "source": [
    "import pandas as pd\n",
    "import folium\n",
    "import math\n",
    "import numpy as np\n",
    "import re\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from gensim.models import CoherenceModel\n",
    "from sklearn.decomposition import LatentDirichletAllocation as LDA\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from gensim.corpora import Dictionary\n",
    "from gensim.models import LdaModel\n",
    "from gensim import corpora\n",
    "import string\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords') # comment out if already downloaded\n",
    "stop_words = set(stopwords.words('english'))\n",
    "nltk.download('punkt')     # comment out if already downloaded\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from gensim import corpora, models\n"
   ]
  },
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     "text": [
      "Mounted at /content/drive\n"
     ]
    }
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   "source": [
    "# drop column\n",
    "from google.colab import drive\n",
    "drive.mount(\"/content/drive\")\n",
    "\n",
    "## for jupyter lab\n",
    "#path=\"Reviews_w_sentiment_score_data.csv\"\n",
    "\n",
    "\n",
    "## for Colab\n",
    "path=\"/content/drive/MyDrive/M2/Reviews_w_sentiment_score_data.csv\"\n",
    "\n",
    "\n",
    "reviews_df = pd.read_csv(path, lineterminator = '\\n')\n",
    "LDA_df = reviews_df.copy()\n",
    "\n",
    "# LDA_df = LDA_df.drop('cleaned_comments_nltk', axis=1)"
   ]
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       "   Unnamed: 0  listing_id reviewer_name  \\\n",
       "0           0         109         Edwin   \n",
       "1           1         109          Jenn   \n",
       "2           2        2708       Kuberan   \n",
       "3           3        2708       Camilla   \n",
       "4           4        2708        Fallon   \n",
       "\n",
       "                                           comments2    id  \\\n",
       "0  The host canceled my reservation the day befor...   109   \n",
       "1  Me and two friends stayed for four and a half ...   109   \n",
       "2  i had a wonderful stay. Everything from start ...  2708   \n",
       "3  Charles is just amazing and he made my stay sp...  2708   \n",
       "4  Staying with Chas was an absolute pleasure. He...  2708   \n",
       "\n",
       "   final_review_score score_label  Sentiment_Score Sentiment  \\\n",
       "0            4.285714        high           0.0000   Neutral   \n",
       "1            4.285714        high           0.9316  Positive   \n",
       "2            4.912857        high           0.8126  Positive   \n",
       "3            4.912857        high           0.9909  Positive   \n",
       "4            4.912857        high           0.9672  Positive   \n",
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       "   normalized_Sentiment_score  normalize_rating_score  \n",
       "0                        2.50                    4.25  \n",
       "1                        4.75                    4.25  \n",
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       "4                        5.00                    5.00  "
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   "source": [
    "LDA_df.head()"
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    "## 1. Exploration / Hyperparameter tuning\n",
    "\n",
    "The goal of the data_cleaning function is to preprocess and clean the input text data to make it suitable for further analysis or modeling. The cleaning process involves multiple steps:\n",
    "\n",
    "1. Converting text to lowercase and removing newline characters.\n",
    "2. Removing symbols and punctuation.\n",
    "3. Removing common and specific stopwords to reduce noise.\n",
    "4. Removing numbers.\n",
    "5. Optionally lemmatizing words to reduce them to their base or root form.\n",
    "6. Finally, the function tokenizes the cleaned text and returns a list of words (tokens).\n",
    "\n",
    "This preprocessing stage is vital for our Air Bnb Natural Language Processing (NLP) task as it helps in reducing the dimensionality of the data, removing unnecessary noise and making the data more suitable for extracting meaningful insights or for use in the LDA model.\n",
    "\n",
    "It is an iterative process. We generate a couple of times the LDA chart to see what other words which came out in the topic modelling which doesn't make sense at all and added them to the existing stopwords we have.\n"
   ]
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    "def data_cleaning(text, stem = \"None\"):\n",
    "\n",
    "    final_string = \"\"\n",
    "\n",
    "    # cleaning\n",
    "    text = text.lower()\n",
    "    text = re.sub(r'\\n', '', text)\n",
    "\n",
    "    # removing symbols and punctuation\n",
    "    translator = str.maketrans('', '', string.punctuation)\n",
    "    text = text.translate(translator)\n",
    "    text = text.split()\n",
    "\n",
    "    # Add stopwords\n",
    "    extra_words = nltk.corpus.stopwords.words(\"english\")\n",
    "    extend_stopwords = ['hi', 'im','i','los', 'angeles','stay','flat','room','place','apartment','u','would','really',\n",
    "                        'airbnb','good','nice','great','didnt','lovely','amazing', 'bnb', 'santa', 'monica', 'well',\n",
    "                        'not', 'highly', 'thank', 'perfect', 'everything', 'not', 'house', 'home', 'can', 'everything', 'ive', 'ever', 'one', 'like', 'felt', 'loved', 'super', 'definitely', 'recommend'\n",
    "                       'back', 'come', 'venice', 'la', 'night', 'get', 'even', 'day']\n",
    "    extra_words.extend(extend_stopwords)\n",
    "\n",
    "    cleaned_text = [word for word in text if not word in extra_words]\n",
    "\n",
    "    # getting rid of numbers\n",
    "    cleaned_text = [re.sub(r'\\w*\\d\\w*', '', w) for w in cleaned_text]\n",
    "\n",
    "    # Stem or Lemmatize\n",
    "    if stem == 'Lem':\n",
    "        lem = WordNetLemmatizer()\n",
    "        text_stemmed = [lem.lemmatize(y) for y in cleaned_text]\n",
    "    else:\n",
    "        text_stemmed = cleaned_text\n",
    "\n",
    "    stem_clean = [word for word in text_stemmed if not word in extra_words]\n",
    "    final_string = ' '.join(stem_clean)\n",
    "\n",
    "    result = nltk.word_tokenize(final_string)\n",
    "\n",
    "    return result"
   ]
  },
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    "# Create new column\n",
    "# functions take a long time to run because of the data size, so we saved it out in a separate csv file.\n",
    "LDA_df['cleaned_comments_nltk'] = LDA_df['comments2'].apply(data_cleaning, stem='Lem')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "-X68logC_Xju",
   "metadata": {
    "id": "-X68logC_Xju"
   },
   "outputs": [],
   "source": [
    "LDA_df.to_csv('/content/drive/MyDrive/M2/LDA_df.csv')"
   ]
  },
  {
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   "id": "1NgZOw7P_ej4",
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    "id": "1NgZOw7P_ej4"
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   "source": [
    "path=\"/content/drive/MyDrive/M2/LDA_df.csv\"\n",
    "\n",
    "LDA_df = pd.read_csv(path)"
   ]
  },
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       "      <td>Edwin</td>\n",
       "      <td>The host canceled my reservation the day befor...</td>\n",
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       "      <td>4.25</td>\n",
       "      <td>['host', 'canceled', 'reservation', 'arrival']</td>\n",
       "      <td>host canceled reservation arrival</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Jenn</td>\n",
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       "      <td>109</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>high</td>\n",
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       "      <td>4.75</td>\n",
       "      <td>4.25</td>\n",
       "      <td>['two', 'friend', 'stayed', 'four', 'half', 'm...</td>\n",
       "      <td>two friend stayed four half month comfortable ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2708</td>\n",
       "      <td>Kuberan</td>\n",
       "      <td>i had a wonderful stay. Everything from start ...</td>\n",
       "      <td>2708</td>\n",
       "      <td>4.912857</td>\n",
       "      <td>high</td>\n",
       "      <td>0.8126</td>\n",
       "      <td>Positive</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>['wonderful', 'start', 'end', 'back']</td>\n",
       "      <td>wonderful start end back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2708</td>\n",
       "      <td>Camilla</td>\n",
       "      <td>Charles is just amazing and he made my stay sp...</td>\n",
       "      <td>2708</td>\n",
       "      <td>4.912857</td>\n",
       "      <td>high</td>\n",
       "      <td>0.9909</td>\n",
       "      <td>Positive</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>['charles', 'made', 'special', 'helpful', 'abs...</td>\n",
       "      <td>charles made special helpful absolutely polite...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2708</td>\n",
       "      <td>Fallon</td>\n",
       "      <td>Staying with Chas was an absolute pleasure. He...</td>\n",
       "      <td>2708</td>\n",
       "      <td>4.912857</td>\n",
       "      <td>high</td>\n",
       "      <td>0.9672</td>\n",
       "      <td>Positive</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>['staying', 'chas', 'absolute', 'pleasure', 'a...</td>\n",
       "      <td>staying chas absolute pleasure accommodating r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-140ea53e-93a3-4a7f-b58b-bf5277d1f901')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "      \n",
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       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "      border-radius: 50%;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
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       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "        const buttonEl =\n",
       "          document.querySelector('#df-140ea53e-93a3-4a7f-b58b-bf5277d1f901 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-140ea53e-93a3-4a7f-b58b-bf5277d1f901');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0  listing_id reviewer_name  \\\n",
       "0             0           0         109         Edwin   \n",
       "1             1           1         109          Jenn   \n",
       "2             2           2        2708       Kuberan   \n",
       "3             3           3        2708       Camilla   \n",
       "4             4           4        2708        Fallon   \n",
       "\n",
       "                                           comments2    id  \\\n",
       "0  The host canceled my reservation the day befor...   109   \n",
       "1  Me and two friends stayed for four and a half ...   109   \n",
       "2  i had a wonderful stay. Everything from start ...  2708   \n",
       "3  Charles is just amazing and he made my stay sp...  2708   \n",
       "4  Staying with Chas was an absolute pleasure. He...  2708   \n",
       "\n",
       "   final_review_score score_label  Sentiment_Score Sentiment  \\\n",
       "0            4.285714        high           0.0000   Neutral   \n",
       "1            4.285714        high           0.9316  Positive   \n",
       "2            4.912857        high           0.8126  Positive   \n",
       "3            4.912857        high           0.9909  Positive   \n",
       "4            4.912857        high           0.9672  Positive   \n",
       "\n",
       "   normalized_Sentiment_score  normalize_rating_score  \\\n",
       "0                        2.50                    4.25   \n",
       "1                        4.75                    4.25   \n",
       "2                        4.50                    5.00   \n",
       "3                        5.00                    5.00   \n",
       "4                        5.00                    5.00   \n",
       "\n",
       "                               cleaned_comments_nltk  \\\n",
       "0     ['host', 'canceled', 'reservation', 'arrival']   \n",
       "1  ['two', 'friend', 'stayed', 'four', 'half', 'm...   \n",
       "2              ['wonderful', 'start', 'end', 'back']   \n",
       "3  ['charles', 'made', 'special', 'helpful', 'abs...   \n",
       "4  ['staying', 'chas', 'absolute', 'pleasure', 'a...   \n",
       "\n",
       "                             cleaned_comments_joined  \n",
       "0                  host canceled reservation arrival  \n",
       "1  two friend stayed four half month comfortable ...  \n",
       "2                           wonderful start end back  \n",
       "3  charles made special helpful absolutely polite...  \n",
       "4  staying chas absolute pleasure accommodating r...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LDA_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4de82c77",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4de82c77",
    "outputId": "4acb67b7-5840-47f6-b64f-149d22a3cecf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1311152, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop line items with nan\n",
    "LDA_df = LDA_df.dropna(subset=['cleaned_comments_nltk'])\n",
    "LDA_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8434b262",
   "metadata": {
    "id": "8434b262"
   },
   "outputs": [],
   "source": [
    "# # replace\n",
    "# LDA_df['cleaned_comments_nltk'] = LDA_df['cleaned_comments_nltk'].astype(str)\n",
    "# LDA_df['cleaned_comments_nltk'] = LDA_df['cleaned_comments_nltk'].str.replace('[', '').str.replace(']', '').str.replace(',', '').str.replace(\"'\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "KqnCds9HEfLt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KqnCds9HEfLt",
    "outputId": "7072ea20-61e6-4d4d-841f-187cd2dd51c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.22.4)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3be9ecc3",
   "metadata": {
    "id": "3be9ecc3"
   },
   "outputs": [],
   "source": [
    "#LDA_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "kcKH4m5PERW2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kcKH4m5PERW2",
    "outputId": "4d104c07-f115-41d3-ebca-1ef4234a95e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['and' 'document' 'first' 'is' 'one' 'second' 'the' 'third' 'this']\n"
     ]
    }
   ],
   "source": [
    "### testing to make sure .get_feature_names_out() works\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "corpus = [\n",
    "    'This is the first document.',\n",
    "    'This document is the second document.',\n",
    "    'And this is the third one.',\n",
    "    'Is this the first document?',\n",
    "]\n",
    "vectorizer = TfidfVectorizer()\n",
    "X = vectorizer.fit_transform(corpus)\n",
    "\n",
    "# Now you should be able to call get_feature_names without any issue\n",
    "print(vectorizer.get_feature_names_out() )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cjWOXsNYWvOu",
   "metadata": {
    "id": "cjWOXsNYWvOu"
   },
   "source": [
    "The goal of the next function count_vectorizer is to transform the text data into numerical vectors which can be used in machine learning models. The transformation is done using the TF-IDF (Term Frequency-Inverse Document Frequency) method, which is a popular technique in Natural Language Processing (NLP).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "whykCSrCrz8l",
   "metadata": {
    "id": "whykCSrCrz8l"
   },
   "outputs": [],
   "source": [
    "count_vectorizer = TfidfVectorizer(\n",
    "                        strip_accents='unicode', #strip_accents='unicode': This removes accents and performs other character normalization during the preprocessing step.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "                        preprocessor=None, #This means that no preprocessor is applied to the data before tokenizing it.\n",
    "                        analyzer='word', #This means that features should be made of word n-grams; i.e., the function will analyze the data at the word level.\n",
    "                        ngram_range=(1, 2),\n",
    "                        min_df=10, #min_df=10: This ignores terms that appear in fewer than 10 documents of the corpus.\n",
    "                        use_idf=True, smooth_idf=True, #These parameters enable the inverse document frequency reweighting, which helps in reducing the weight of words that occur very frequently in the corpus.\n",
    "                        max_features = 5000) #This limits the number of features (vocabulary size) to the top 5000, ranked by term frequency across the corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "tmwuooPhqaEu",
   "metadata": {
    "id": "tmwuooPhqaEu"
   },
   "outputs": [],
   "source": [
    "def print_topics(model, count_vectorizer, n_top_words):\n",
    "    words = count_vectorizer.get_feature_names_out()\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(f\"\\nTopic #{topic_idx+1}:\")\n",
    "        print(\" \".join([words[i]\n",
    "                        for i in topic.argsort()[:-n_top_words - 1:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "pXLRuqTYqdlc",
   "metadata": {
    "id": "pXLRuqTYqdlc"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "EQ_54RpKXlAX",
   "metadata": {
    "id": "EQ_54RpKXlAX"
   },
   "source": [
    "After the TfidfVectorizer is defined, we used .fit() method on it with our corpus as the argument, to transform the air bnb text data into a matrix of TF-IDF features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2a17e308",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "id": "2a17e308",
    "outputId": "617789ba-3eb9-48c1-85dc-be11894dbf17"
   },
   "outputs": [
    {
     "data": {
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       "                strip_accents=&#x27;unicode&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(max_features=5000, min_df=10, ngram_range=(1, 2),\n",
       "                strip_accents=&#x27;unicode&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer(max_features=5000, min_df=10, ngram_range=(1, 2),\n",
       "                strip_accents='unicode')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Joining the words into a single string for each comment\n",
    "LDA_df['cleaned_comments_joined'] = LDA_df['cleaned_comments_nltk'].apply(' '.join)\n",
    "\n",
    "# Now fit the count vectorizer on the joined comments\n",
    "count_vectorizer.fit(LDA_df['cleaned_comments_joined'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4hIpUG7v5z-N",
   "metadata": {
    "id": "4hIpUG7v5z-N"
   },
   "outputs": [],
   "source": [
    "### Corpora and Dictionary are from the Gennism module\n",
    "\n",
    "dictionary = corpora.Dictionary(LDA_df['cleaned_comments_nltk'])\n",
    "models\n",
    "doc_term_matrix = [dictionary.doc2bow(text) for text in LDA_df['cleaned_comments_nltk']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "kRCnsRGpYwej",
   "metadata": {
    "id": "kRCnsRGpYwej"
   },
   "source": [
    "## 2. Sensitivity analysis - Coherence score / perplexity score\n",
    "\n",
    "The following function was used to calculate the coherence score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1PWytM-P52YT",
   "metadata": {
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    "outputId": "73b3f421-281a-4719-b324-9b098dc6888e"
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     "text": [
      "Calculating Coherence:   0%|          | 0/5 [00:00<?, ?it/s]<ipython-input-17-b657c5d932c6>:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({'Num_Topics': num_topics, 'Coherence_Score': coherence}, ignore_index=True)\n",
      "Calculating Coherence:  20%|██        | 1/5 [07:08<28:32, 428.14s/it]<ipython-input-17-b657c5d932c6>:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({'Num_Topics': num_topics, 'Coherence_Score': coherence}, ignore_index=True)\n",
      "Calculating Coherence:  40%|████      | 2/5 [16:29<25:19, 506.57s/it]<ipython-input-17-b657c5d932c6>:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({'Num_Topics': num_topics, 'Coherence_Score': coherence}, ignore_index=True)\n",
      "Calculating Coherence:  60%|██████    | 3/5 [26:43<18:30, 555.44s/it]<ipython-input-17-b657c5d932c6>:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({'Num_Topics': num_topics, 'Coherence_Score': coherence}, ignore_index=True)\n",
      "Calculating Coherence:  80%|████████  | 4/5 [38:05<10:05, 605.65s/it]<ipython-input-17-b657c5d932c6>:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({'Num_Topics': num_topics, 'Coherence_Score': coherence}, ignore_index=True)\n",
      "Calculating Coherence: 100%|██████████| 5/5 [50:27<00:00, 605.59s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "# Range to find the best number of topics\n",
    "start = 2\n",
    "stop = 12\n",
    "step = 2\n",
    "\n",
    "def calculate_coherence(lda_model, texts, dictionary):\n",
    "\n",
    "    coherence_model = CoherenceModel(model=lda_model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "    return coherence_model.get_coherence()\n",
    "\n",
    "# Initialize a DataFrame to store the results\n",
    "results_df = pd.DataFrame(columns=['Num_Topics', 'Coherence_Score'])\n",
    "\n",
    "# Apply to LDA model\n",
    "for num_topics in tqdm(range(start, stop, step), desc=\"Calculating Coherence\"):\n",
    "\n",
    "\n",
    "    lda_model = models.LdaModel(corpus=doc_term_matrix, id2word=dictionary, num_topics=num_topics, random_state=42)\n",
    "    coherence = calculate_coherence(lda_model, LDA_df['cleaned_comments_nltk'], dictionary)\n",
    "    # Append results to the DataFrame\n",
    "    results_df = results_df.append({'Num_Topics': num_topics, 'Coherence_Score': coherence}, ignore_index=True)\n"
   ]
  },
  {
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   "execution_count": 20,
   "id": "nltTSxhsMb1R",
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    "results_df.to_csv('/content/drive/MyDrive/M2/Coherence_Score_by_Topic.csv')"
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       "   Num_Topics  Coherence_Score\n",
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    "path=\"/content/drive/MyDrive/M2/Coherence_Score_by_Topic.csv\"\n",
    "\n",
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oUL734enpCeQucNdz/PhxHBwccsrdFf7+/pQrVy6nnFzx9y9EAOXLl8/1xToyMpLly5fj4+OT66dLly7A5dnh/qpq1aq5HkdFRWEYBi+//HKebYwfP/6q2/h7rvLlywPk5Dp8+DBAzqlsVxMZGQlcntb67/v97LPPSE9PJykp6Zqvh8tffqdPn050dDQHDx7ko48+wsfHh1deeYXPP//8hrNcGffatWvnea5u3bo5pe+v/j6OkZGRGIZBzZo187yf/fv35xnDv7ty3dnChQtzli1cuJBGjRpRq1YtAFxcXHj77bdZtmwZfn5+dOjQgXfeeeeqp7X+kwkTJhATE5Pnup2CcnV1zVXaAby8vKhcuXKuL/JXll+tHNasWTPX47JlyxIQEJBzut+Vz0znzp3zjPHvv/+eZ4wdHR2pXLnyP2aPj48nLS3tmr9/m832j9fN5de1PnPOzs5Uq1Ytz/8LKlWqlGcyjiufi2tdb3elwF7vsw+Xrxvds2cPQUFBtGjRggkTJly1/IqI/Wi2QBEp8vbs2YOvr29OAfruu+9IT0/n/fff5/3338+z/rx585g4cWK+9uHp6UmlSpVyJhu4UX//snktVqv1qsuNv1yDYrPZ6Nq1K88///xV173yBeyKv88Qd+Xo0LPPPkv37t2vuo2/l8EbyfVPruz33XffpVGjRldd50anUrdYLNSqVYtatWpx5513UrNmTebNm5frSEphu9o4WiwWli1bdtXx+af34uLiknON2yeffEJsbCwbN25k0qRJudZ75plnuPvuu/npp5/47bffePnll3nzzTdZvXo1jRs3vuH8HTp0oFOnTrzzzjt5jsLBtT+j2dnZV11+rc9EYXxWrrjymZk7dy7+/v55nnd0zP31xMXFJddRIbm6Pn360L59e3788Ud+//133n33Xd5++21++OEHevbsaXY8kVJB5UpEirTNmzdz+PDhXNO0z5s3j9DQ0JyjMX81c+ZM5s+fn+9yBXDXXXfx6aefsnnzZlq3bn3ddYODg7HZbERGRua6101sbCyJiYk5M5HlR/Xq1UlNTc05UpVfV06DcnJyKvA2rpYJLhfcvxezv6/j6elZaPuFy++nfPnyREdH58lyrf1cGfeDBw/mee7AgQNUrFjxH6fwrl69OoZhULVq1TyF9kb17duXL7/8klWrVrF//34Mw7jqja6rV6/Of/7zH/7zn/8QGRlJo0aNeP/99/n666/ztb8JEybQqVMnZs6cmee5K0cj/z4Rx9+PqBSmyMhIbr/99pzHqampREdH06tXL+D/f5e+vr6F+pnx8fGhTJky1/z9Ozg4EBQUVGj7g9yfub+eipiRkcHRo0fzvL8zZ87kmUr+ytH2a81YeGW7N/KPPwEBATz11FM89dRTxMXF0aRJE9544w2VK5FbRP8MJCJF1vHjxxk4cCDOzs4899xzAJw8eZL169fTp08fHnzwwTw/gwYNIioqKmdWMLjxqdiff/553N3dGTJkCLGxsXmeP3z4cM6U31e+JP59hrEPPvgAuDxrXH716dOHzZs389tvv+V5LjExkaysrOu+3tfXN+cL9pVC8ldXm2L9n3Tr1g0PDw/efPNNLl26lOu5K0csmjZtSvXq1XnvvfdyTuHMz37DwsLynKoHsHXrVs6dO5dzulWTJk2oWrUqU6ZMyVMUrmQJCAigUaNGfPnll7nW2bNnD7///nvO7+167r//fqxWKxMnTsxzVMYwDM6dO/eP2+jSpQve3t4sXLiQhQsX0qJFi1ynH6alpeUZz+rVq+Ph4XHNqfyvp2PHjnTq1Im33347z3aDg4OxWq15rtn75JNP8r2fG/Xpp5/mmiVv+vTpZGVl5XzB7969O56enkyaNCnPbHpQsM8qXD661q1bN37++edcp9jFxsYyf/582rVrl3MEvLB06dIFZ2dnPvroo1yfl88//5ykpKQ8/y/IysrKVYIzMjKYOXMmPj4+NG3a9Kr78PHxoUOHDsyePTvP/8uu7DM7OzvP6be+vr5UqlSpQJ8pESkYHbkSEbubPXt2rmm7r/jrPaPCw8P5+uuvsdlsJCYmsm3bNr7//nssFgtz586lQYMGAMyfPx/DMLjnnnuuuq9evXrh6OjIvHnzaNmyJXD5WouOHTv+46QW1atXZ/78+fTt2zdn+vbQ0FAyMjLYtGkT3333HQMHDgSgYcOGPPbYY3z66ackJibSsWNHtm7dypdffknv3r1z/av9jXruuedYvHgxd911FwMHDqRp06ZcuHCB3bt3s2jRIo4dO0bFihWvu42PP/6Ydu3aUb9+fYYOHUq1atWIjY1l8+bNnDp1ioiIiHxl8vT0ZPLkyQwZMoTmzZvn3A8qIiKCtLQ0vvzySxwcHPjss8/o2bMn9erVY9CgQQQGBnL69GnWrFmDp6dnrumi/27u3LnMmzeP++67j6ZNm+Ls7Mz+/fuZPXs2rq6uvPjiiwA4ODgwffp07r77bho1asSgQYMICAjgwIED7N27N6eUvvvuu/Ts2ZPWrVvz73//m4sXLzJ16lS8vLyYMGHCP77n6tWr8/rrrzNu3DiOHTtG79698fDw4OjRo/z44488/vjjPPvss9fdhpOTE/fffz/ffPMNFy5c4L333sv1/KFDh7jjjjvo06cPISEhODo68uOPPxIbG3vVqfxvxPjx46/6ufPy8uKhhx5i6tSpWCwWqlevzq+//vqP147djIyMjJz3d/DgQT755BPatWuX8+fW09OT6dOn869//YsmTZrw8MMP4+Pjw4kTJ1iyZAlt27Zl2rRpBdr366+/zooVK2jXrh1PPfUUjo6OzJw5k/T09KveL+9m+fj4MG7cOCZOnEiPHj245557ct5z8+bN89wcvVKlSrz99tscO3aMWrVqsXDhQnbu3Mmnn36aaxKQv/voo49o164dTZo04fHHH6dq1aocO3aMJUuWsHPnTlJSUqhcuTIPPvggDRs2pGzZsqxcuZJt27Zd9fRpEbGTWz9BoYiUFlemZb7Wz8mTJ3OmHL7y4+joaHh7exstW7Y0xo0bl2fa4fr16xu33XbbdffbqVMnw9fX18jMzDQM48amYv+rQ4cOGUOHDjWqVKliODs7Gx4eHkbbtm2NqVOn5priPDMz05g4caJRtWpVw8nJyQgKCjLGjRuXax3DyDs19hUdO3bMkyslJcUYN26cUaNGDcPZ2dmoWLGi0aZNG+O9997LM03ztaaQPnz4sDFgwADD39/fcHJyMgIDA4277rrLWLRoUc4615om/1rTdi9evNho06aN4ebmZnh6ehotWrQwFixYkGudHTt2GPfff79RoUIFw8XFxQgODjb69OljrFq16uoD/T+7du0ynnvuOaNJkyaGt7e34ejoaAQEBBgPPfSQER4enmf9DRs2GF27djU8PDwMd3d3o0GDBsbUqVNzrbNy5Uqjbdu2OXnvvvtuY9++fbnW+euU5lfz/fffG+3atTPc3d0Nd3d3o06dOsbw4cONgwcPXvf9XLFixQoDMCwWi3Hy5Mlcz509e9YYPny4UadOHcPd3d3w8vIyWrZsaXz77bf/uN3r5b4y1fffP2/x8fHGAw88YJQpU8YoX7688cQTTxh79uy56lTs7u7uV91uvXr18iz/+2f7yudq3bp1xuOPP26UL1/eKFu2rPHII4/kmhr/ijVr1hjdu3c3vLy8DFdXV6N69erGwIEDjT///PMfM11PeHi40b17d6Ns2bJGmTJljNtvv93YtGlTrnUKayr2K6ZNm2bUqVPHcHJyMvz8/Ixhw4bluWXAlXH8888/jdatWxuurq5GcHCwMW3atKtm++vvxjAMY8+ePcZ9991nlCtXznB1dTVq165tvPzyy4ZhXJ7S/bnnnjMaNmyY82ejYcOGxieffJKv9yciN8diGAW4ElVERERE8qVTp06cPXs23xPniEjxoWuuRERERERECoHKlYiIiIiISCFQuRIRERERESkEuuZKRERERESkEOjIlYiIiIiISCFQuRIRERERESkEuonwVdhsNs6cOYOHhwcWi8XsOCIiIiIiYhLDMEhJSaFSpUo4OFz/2JTK1VWcOXOGoKAgs2OIiIiIiEgRcfLkSSpXrnzddVSursLDwwO4PICenp6mZsnMzOT333+nW7duODk5mZqlJNL42p/G2L40vval8bUvja99aXztS+NrX0VpfJOTkwkKCsrpCNejcnUVV04F9PT0LBLlqkyZMnh6epr+wSqJNL72pzG2L42vfWl87Uvja18aX/vS+NpXURzfG7lcSBNaiIiIiIiIFAKVKxERERERkUKgciUiIiIiIlIIVK5EREREREQKgcqViIiIiIhIIVC5EhERERERKQQqVyIiIiIiIoVA5UpERERERKQQFIly9fHHH1OlShVcXV1p2bIlW7duve76iYmJDB8+nICAAFxcXKhVqxZLly696rpvvfUWFouFZ555xg7JRURERERELnM0O8DChQsZM2YMM2bMoGXLlkyZMoXu3btz8OBBfH1986yfkZFB165d8fX1ZdGiRQQGBnL8+HHKlSuXZ91t27Yxc+ZMGjRocAveiYiIiIiIlGaml6sPPviAoUOHMmjQIABmzJjBkiVLmD17NmPHjs2z/uzZs0lISGDTpk04OTkBUKVKlTzrpaam8sgjjzBr1ixef/3162ZIT08nPT0953FycjIAmZmZZGZmFvStFYor+zc7R0ml8bU/jbF9aXztS+NrXxpf+9L42pfG176K0vjmJ4PFMAzDjlmuKyMjgzJlyrBo0SJ69+6ds/yxxx4jMTGRn3/+Oc9revXqhbe3N2XKlOHnn3/Gx8eH/v3788ILL2C1WnNtw9vbm8mTJ9OpUycaNWrElClTrppjwoQJTJw4Mc/y+fPnU6ZMmZt+nyIiIiIiUjylpaXRv39/kpKS8PT0vO66ph65Onv2LNnZ2fj5+eVa7ufnx4EDB676miNHjrB69WoeeeQRli5dSlRUFE899RSZmZmMHz8egG+++Ybw8HC2bdt2QznGjRvHmDFjch4nJycTFBREt27d/nEA7S0zM5MVK1bQtWvXnCN1Ung0vvanMbYvja99aXztS+NrXxpf+9L42ldRGt8rZ7XdCNNPC8wvm82Gr68vn376KVarlaZNm3L69Gneffddxo8fz8mTJxk1ahQrVqzA1dX1hrbp4uKCi4tLnuVOTk6m/zKvKEpZSiKNr/1pjO1L41v4sm0G4UcT2H7WQoVTKbSu4YvVwWJ2rBJJn1/70vjal8bXvorC+OZn/6aWq4oVK2K1WomNjc21PDY2Fn9//6u+JiAgACcnp1ynANatW5eYmBgyMjLYvn07cXFxNGnSJOf57Oxs1q9fz7Rp00hPT8/1WhERkb9bvieaib/sIzrpEmDlq8g/CfByZfzdIfQIDTA7noiIFFGmTsXu7OxM06ZNWbVqVc4ym83GqlWraN269VVf07ZtW6KiorDZbDnLDh06REBAAM7Oztxxxx3s3r2bnTt35vw0a9aMRx55hJ07d6pYiYjIdS3fE82wr8P/V6z+X0zSJYZ9Hc7yPdEmJRMRkaLO9PtcjRkzhlmzZvHll1+yf/9+hg0bxoULF3JmDxwwYADjxo3LWX/YsGEkJCQwatQoDh06xJIlS5g0aRLDhw8HwMPDg9DQ0Fw/7u7uVKhQgdDQUFPeo4iIFA/ZNoOJv+zjajM9XVk28Zd9ZNtMmwtKRESKMNOvuerbty/x8fG88sorxMTE0KhRI5YvX54zycWJEydwcPj/DhgUFMRvv/3G6NGjadCgAYGBgYwaNYoXXnjBrLcgIiIlxNajCXmOWP2VAUQnXWLr0QRaV69w64KJiEixYHq5AhgxYgQjRoy46nNr167Ns6x169Zs2bLlhrd/tW2IiIj8XVzKtYtVQdYTEZHSxfTTAkVERIqKimXzzhx7NT43uJ6IiJQuKlciIiKAzWbw/fZTN7TuR6siOZmQZudEIiJS3KhciYhIqWezGbz4425+2HGaK7ey+vsdra48drY6sOVoAj2mrGde2HEMQ5NbiIjIZSpXIiJSqhmGwfjFe/lm20kcLDDl4cbMeLQJ/l65b0Tv7+XKjEebsGJMB1pU9eZCRjYv/biHAbO3cibxoknpRUSkKCkSE1qIiIiYwTAMXvt1P3O3HMdigfceasg9DSsB0DXEn81Rcfz+Rxjd2rekdQ1frP87rPXN0FbM2XSMd347wB+RZ+k+eT0v3xXCQ80qY7H8/ZiXiIiUFjpyJSIipZJhGLy9/CCzNx4F4K3763N/k8o5z1sdLLSs6k3TigYtq3rnFCsABwcLg9tVZenT7WlyWzlS0rN4/vtdDJ6zjZjrTOUuIiIlm8qViIiUSpNXRjJj3WEAXusdSt/mt+V7G9V8yvLdk214sVcdnB0dWHMwnm6T1/FD+CldiyUiUgqpXImISKkzbXUkH62KBOCVu0L4V6vgAm/L6mDh8Q7VWfp0OxpW9iL5UhZjvo1g6FfbdT8sEZFSRuVKRERKlZnrDvPe74cAGNezDoPbVS2U7dbw9eD7YW14rnttnKwWVu6Ppdvk9SyOOKOjWCIipYTKlYiIlBqzNxzlzWUHAHi2Wy2e6Fi9ULfvaHVg+O01+GVkO+pV8iQxLZOnF+zgqXnhnEtNL9R9iYhI0aNyJSIipcLXW47z6q/7AHi6cw1GdK5pt33V8ffkp+FtGd2lFo4OFpbtiaHb5PUs2x1tt32KiIj5VK5ERKTE+3bbSf770x4AnuhYjdFda9l9n05WB0Z1qcnPI9pSx9+DcxcyGDYvnJELdnD+Qobd9y8iIreeypWIiJRoP4Sf4oUfdgEwuG1Vxvaoc0vvRVWvkheLR7RjZOcaWB0s/BJxhq6T17NiX+wtyyAiIreGypWIiJRYv0Sc4dnvIjAM+FerYF6+q64pN/l1dnTgP91q8+NTbajpW5azqekM/epPxizcSVJa5i3PIyIi9qFyJSIiJdLyPdE8s3AnNgMebh7ExHvqmVKs/qpB5XL8MrIdT3asjoMFfthxmm5T1rHmQJypuUREpHCoXImISImzcl8sIxfsINtmcH+TQCbdVx8HB3OL1RWuTlbG9qzDomFtqObjTmxyOoPmbOP5RREkX9JRLBGR4kzlSkRESpS1B+N4al44mdkG9zSsxLsPNiwyxeqvmtxWnqVPt2dIu6pYLPDtn6foMXk9f0TGmx1NREQKSOVKRERKjI1RZ3li7nYysm30DPXngz4NsRbBYnWFq5OV/94VwrdPtCa4QhnOJF3iX59v5cUfd5OanmV2PBERySeVKxERKRHCjpzj319uIz3LRpe6fnz4cGMcrcXjr7nmVbxZNqo9A9tUAWB+2Al6TFnPpsNnzQ0mIiL5Ujz+1hEREbmO7cfPM3jONi5l2uhU24ePH2mMs2Px+iuujLMjE+6px4Khrahc3o1T5y/Sf1YY43/eQ1qGjmKJiBQHxetvHhERkb+JOJnIwNlbuZCRTbsaFZnxaFNcHK1mxyqw1tUr8NszHXik5W0AfLn5OD2m/MHWowkmJxMRkX+iciUiIsXWntNJ/OvzMFLSs2hZ1ZtZA5rh6lR8i9UV7i6OvHFffeb+uwWVvFw5kZBG308389qv+7iUmW12PBERuQaVKxERKZYOxCTzr8/DSL6URdPg8swe2Bw35+JfrP6qfU0flo/uwMPNgzAM+HzDUXp9+Afbj583O5qIiFyFypWIiBQ7UXEpPDIrjPNpmTQMKscXg5rj7uJodiy78HR14q0HGvDFoOb4ebpw5OwFHpqxiTeX7ddRLBGRIkblSkREipUj8an0mxXGuQsZ1KvkyVeDWuDp6mR2LLu7vbYvvz/TkfubBGIzYOa6I9w1dQMRJxPNjiYiIv+jciUiIsXGiXNp9J8VRnxKOnX8Pfj63y3xKlPyi9UVXmWc+KBPI2YNaIaPhwtRcancP30T7/12kPQsHcUSETGbypWIiBQLp86n0W/WFmKSL1HTtyxfD2lJeXdns2OZomuIH78/04F7G1Ui22YwbU0U907byJ7TSWZHExEp1VSuRESkyItOunzPp9OJF6lW0Z15Q1pSsayL2bFMVd7dmQ8fbsz0R5pQwd2ZAzEp9P54I1NWHiIz22Z2PBGRUknlSkREirS45Es8MiuMEwlp3OZdhvlDW+Hr6Wp2rCKjZ/0Afh/dgV71/cmyGUxZGUnvjzeyPzrZ7GgiIqWOypWIiBRZZ1PT6f9ZGEfOXiCwnBvzh7bE30vF6u8qlHXhk0eaMrVfY8qVcWLvmWTumbaBaasjydJRLBGRW0blSkREiqTzFzJ49LMwouJS8fd0ZcHQVlQuX8bsWEXa3Q0r8fvoDnQN8SMz2+C93w9x//RNRMammB1NRKRUULkSEZEiJyktk0c/D+NATAo+Hi4seLwVt1VQsboRvh6ufPqvpkzu2xBPV0d2nUrizqkbmLnuMNk2w+x4IiIlmsqViIgUKSmXMhnwxVb2nkmmgrszC4a2pGpFd7NjFSsWi4X7GldmxZiO3F7bh4wsG28uO8CDMzZxOD7V7HgiIiWWypWIiBQZF9KzGPjFNiJOJlK+jBPzhrakhq+H2bGKLT9PV2YPbM47DzbAw8WRHScS6fXhH3z2xxFsOoolIlLoVK5ERKRIuJiRzeA529h+/Dyero7M/XdL6vh7mh2r2LNYLPRpFsRvozvQvmZF0rNsvL5kPw9/uoVjZy+YHU9EpERRuRIREdNdysxm6Fd/EnY0AQ+Xy8UqNNDL7FglSqVybnw1uAWT7quPu7OVrccS6PnhH3y56ZiOYomIFBKVKxERMVV6VjZPzN3OhqizuDtbmTO4OQ2Dypkdq0SyWCz0b3kby5/pQOtqFbiYmc34xXt55LMwTiakmR1PRKTYU7kSERHTZGTZGD4vnHWH4nFzsjJ7YHOaBnubHavEC/Iuw7whLXn13nq4OVnZfOQcPaasZ17YcQxDR7FERApK5UpEREyRlW1j1Dc7WLk/DhdHBz57rBktq1UwO1ap4eBgYUDrKix/pj0tqnhzISObl37cw4DZWzmTeNHseCIixZLKlYiI3HLZNoPR30awbE8MzlYHPh3QjLY1Kpodq1QKruDON4+34uW7QnBxdOCPyLN0n7yeb7ed1FEsEZF8UrkSEZFbymYzeG5RBL9EnMHRwcInjzShYy0fs2OVag4OFv7drirLRrWnyW3lSEnP4vnvdzF4zjZiki6ZHU9EpNhQuRIRkVvGZjN48cfd/BB+GquDhWn9G9MlxM/sWPI/1XzK8t2TbXixVx2cHR1YczCebpPX8UP4KR3FEhG5ASpXIiJySxiGwfjFe/lm20kcLDClbyN6hAaYHUv+xupg4fEO1Vkysh0NK3uRfCmLMd9G8Pjc7cSl6CiWiMj1qFyJiIjdGYbBa7/uZ+6W41gs8N5DDbm7YSWzY8l11PTz4PthbXiue22crBZW7Iul2+T1LI44o6NYIiLXoHIlIiJ2ZRgGby0/wOyNRwF4+/4G3N+kssmp5EY4Wh0YfnsNfhnZjnqVPElMy+TpBTt4al4451LTzY4nIlLkqFyJiIhdTV5xiJnrjgDweu9Q+jQPMjmR5Fcdf09+Gt6W0V1q4ehgYdmeGLpNXs+y3dFmRxMRKVJUrkRExG6mrorko9VRAIy/O4RHWwWbnEgKysnqwKguNflpeFvq+Htw7kIGw+aF8/SCHZy/kGF2PBGRIkHlSkRE7GLmusO8v+IQAC/2qsOgtlVNTiSFITTQi8Uj2jGycw2sDhYWR5yh6+T1rNgXa3Y0ERHTqVyJiEihm73hKG8uOwDAs91q8XiH6iYnksLk7OjAf7rV5odhbajpW5azqekM/epPxizcSVJaptnxRERMo3IlIiKFau6W47z66z4Anu5cgxGda5qcSOylYVA5fhnZjic7VsfBAj/sOE23KetYcyDO7GgiIqZQuRIRkUKzcNsJXv5pDwBPdqzO6K61TE4k9ubqZGVszzp892QbqlV0JzY5nUFztvH8ogiSL+koloiULipXIiJSKH4IP8XYH3YDMLhtVV7oURuLxWJyKrlVmgaXZ+mo9gxpVxWLBb798xQ9Jq/nj8h4s6OJiNwyKlciInLTfok4w7PfRWAY8K9Wwbx8V10Vq1LI1cnKf+8KYeHjrQmuUIYzSZf41+dbefHH3aSmZ5kdT0TE7lSuRETkpizfE80zC3diM+Dh5kFMvKeeilUp16KqN8tGtWdgmyoAzA87QY8p69l0+Ky5wURE7EzlSkRECmzlvlhGzN9Bts3ggSaVmXRffRwcVKwEyjg7MuGeeiwY2orK5d04df4i/WeFMf7nPaRl6CiWiJRMKlciIlIgaw/G8dS8cLJsBvc0rMQ7DzZQsZI8WlevwPJnOvBIy9sA+HLzcXp++AdbjyaYnExEpPCpXImISL5tjDrL43O3k5Fto2eoPx/0aYhVxUquoayLI2/cV5+5/25BJS9Xjp9Lo++nm5m07CAZ2WanExEpPCpXIiKSL1uOnOPfX24jI8tGl7p+fNSvMY5W/XUi/6x9TR+Wj+5A32ZBGAZ8sek47+6ysuNkotnRREQKhf42FBGRG7b9eAKD52zjUqaNTrV9+PiRxjipWEk+eLo68faDDfhiUHP8PFyIu2Th4VlbeXPZfi5l6jCWiBRv+htRRERuSMTJRAbO3kZaRjbtalRkxqNNcXG0mh1Liqnba/uyZGQbmvvYsBkwc90R7p66gQgdxRKRYkzlSkRE/tGe00n86/MwUtKzaFnVm1kDmuHqpGIlN8fLzYlHa9iY0b8RFcu6EBmXyv3TN/HebwdJz9JRLBEpflSuRETkug7EJPPo52EkX8qiWXB5Zg9sjpuzipUUnjvq+rJidAfuaViJbJvBtDVR3DttI3tOJ5kdTUQkX1SuRETkmiJjU3hkVhiJaZk0DCrHF4Oa4+7iaHYsKYHKuzvzUb/GTH+kCd7uzhyISaH3xxuZsvIQmdk2s+OJiNwQlSsREbmqI/Gp9P8sjHMXMggN9OSrQS3wcHUyO5aUcD3rB/D76A70DPUny2YwZWUkvT/eyIGYZLOjiYj8I5UrERHJ4/i5C/SfFUZ8Sjp1/D2YO7glXmVUrOTWqFjWhU8eacLUfo0pV8aJvWeSuXvqBj5eE0WWjmKJSBGmciUiIrmcOp9G/1lhxCRfoqZvWb4e0pLy7s5mx5JSxmKxcHfDSvw+ugNd6vqRmW3w7m8HeWD6JiJjU8yOJyJyVSpXIiKSIzrpIv1nhXE68SLVKrozb2hLKpZ1MTuWlGK+Hq7MGtCUyX0b4unqSMSpJO6cuoGZ6w6TbTPMjicikovKlYiIABCXfIn+s8I4kZDGbd5lmD+0Fb4ermbHEsFisXBf48qsGNOR22v7kJFl481lB3hoxiaOxKeaHU9EJIfKlYiIcDY1nf6fhXH07AUCy7kxf2hL/L1UrKRo8fN0ZfbA5rzzYAM8XBwJP5FIzw//4PMNR7HpKJaIFAEqVyIipdz5Cxk8+lkYUXGpBHi5smBoKyqXL2N2LJGrslgs9GkWxPLRHWhfsyLpWTZe+3UfD3+6hePnLpgdT0RKOZUrEZFSLCktk0c/D+NATAq+Hi7MH9qK2yqoWEnRF1jOja8Gt2DSffVxd7ay9VgCPab8wVebj+koloiYRuVKRKSUSr6UyYDZYew9k0zFss7MH9qSqhXdzY4lcsMsFgv9W97G8mc60LpaBS5mZvPKz3t55LMwTiakmR1PREohlSsRkVIoNT2LQV9sI+JUEuXLOPH1kJbU8PUwO5ZIgQR5l2HekJa8em893JysbD5yjh5T1jM/7ASGoaNYInLrqFyJiJQyaRlZDJ6zje3Hz+Pp6sjcf7ekjr+n2bFEboqDg4UBrauw/Jn2tKjizYWMbF78cTcDZm/lTOJFs+OJSCmhciUiUopcysxm6Fd/svVoAh4ul4tVaKCX2bFECk1wBXe+ebwVL98VgoujA39EnqX75PV8++dJHcUSEbsrEuXq448/pkqVKri6utKyZUu2bt163fUTExMZPnw4AQEBuLi4UKtWLZYuXZrz/Jtvvknz5s3x8PDA19eX3r17c/DgQXu/DRGRIi09K5sn5m5nY9Q53J2tzBncgoZB5cyOJVLoHBws/LtdVZaOak+T28qRkp7F84t2MXjONmKTL5kdT0RKMNPL1cKFCxkzZgzjx48nPDychg0b0r17d+Li4q66fkZGBl27duXYsWMsWrSIgwcPMmvWLAIDA3PWWbduHcOHD2fLli2sWLGCzMxMunXrxoULmqJVREqnjCwbw+eFs+5QPG5OVmYPbE7T4PJmxxKxq+o+ZfnuyTaM61kHZ0cH1hyMp+sH6/hxxykdxRIRu3A0O8AHH3zA0KFDGTRoEAAzZsxgyZIlzJ49m7Fjx+ZZf/bs2SQkJLBp0yacnJwAqFKlSq51li9fnuvxnDlz8PX1Zfv27XTo0ME+b0REpIjKzLbx9IIdrNwfh4ujA58/1oyW1SqYHUvklrA6WHiiY3U61/Hl2e8iiDiVxOiFESzdHcMb94Xi66GbZYtI4TG1XGVkZLB9+3bGjRuXs8zBwYEuXbqwefPmq75m8eLFtG7dmuHDh/Pzzz/j4+ND//79eeGFF7BarVd9TVJSEgDe3t5XfT49PZ309PScx8nJyQBkZmaSmZlZoPdWWK7s3+wcJZXG1/40xvb1T+ObbTP4z6LdLN8bg5PVwvT+jWge7KXfxw3S59e+buX4VvF25ZshzZm14RhT1xxmxb5Yth1NYPxddbizvj8Wi8XuGW41fX7tS+NrX0VpfPOTwWKYeFz8zJkzBAYGsmnTJlq3bp2z/Pnnn2fdunWEhYXleU2dOnU4duwYjzzyCE899RRRUVE89dRTPP3004wfPz7P+jabjXvuuYfExEQ2bNhw1RwTJkxg4sSJeZbPnz+fMmV0M00RKZ5sBsw/7MC2eAesFoPBtW2EltepUCJnLsC8w1ZOXbhcqBp523iomo2yTiYHE5EiKS0tjf79+5OUlISn5/Vn1zX9tMD8stls+Pr68umnn2K1WmnatCmnT5/m3XffvWq5Gj58OHv27LlmsQIYN24cY8aMyXmcnJxMUFAQ3bp1+8cBtLfMzExWrFhB165dc06DlMKj8bU/jbF9XWt8bTaDlxfvY1v8aawOFj7q25BuIX4mJi2e9Pm1LzPH97FsGzPWHeWTdUfYmeDAiXQXJt4dQo96JefPiT6/9qXxta+iNL5Xzmq7EaaWq4oVK2K1WomNjc21PDY2Fn9//6u+JiAgACcnp1ynANatW5eYmBgyMjJwdnbOWT5ixAh+/fVX1q9fT+XKla+Zw8XFBRcXlzzLnZycTP9lXlGUspREGl/70xjb11/H1zAMXv55D99uP42DBab0bcSdDSuZnLB40+fXvswYXycnGNO9Dt1CA3j2uwgOxKQw8psI7mlYiYn31KO8u/M/b6SY0OfXvjS+9lUUxjc/+zd1tkBnZ2eaNm3KqlWrcpbZbDZWrVqV6zTBv2rbti1RUVHYbLacZYcOHSIgICCnWBmGwYgRI/jxxx9ZvXo1VatWte8bEREpIgzD4NVf9/H1lhNYLPB+n4bcrWIlck2hgV78PKItI26vgdXBwuKIM3SdvJ4V+2L/+cUiIn9j+lTsY8aMYdasWXz55Zfs37+fYcOGceHChZzZAwcMGJBrwothw4aRkJDAqFGjOHToEEuWLGHSpEkMHz48Z53hw4fz9ddfM3/+fDw8PIiJiSEmJoaLF3WHdhEpuQzD4K3lB/hi4zEA3r6/Afc1vvZRexG5zMXRyrPda/PDsDbU8C3L2dR0hn71J2O+3UlSmvkX04tI8WH6NVd9+/YlPj6eV155hZiYGBo1asTy5cvx87t8zvOJEydwcPj/DhgUFMRvv/3G6NGjadCgAYGBgYwaNYoXXnghZ53p06cD0KlTp1z7+uKLLxg4cKDd35OIiBkmrzjEzHVHAHi9dyh9mgeZnEikeGkYVI5fR7Zj8spDzFp/hB/CT7Mx6ixvPdCA22v7mh1PRIoB08sVXL42asSIEVd9bu3atXmWtW7dmi1btlxze7oxoIiUNh+vPcJHq6MAGH93CI+2CjY5kUjx5OpkZVzPunQL8ee57yI4cvYCg77YRt9mQbx0V108XXVtjYhcm+mnBYqIyM1ZddrClFWXi9WLveowqK2uMxW5WU2Dy7N0VHv+3a4qFgss/PMkPSav54/IeLOjiUgRpnIlIlKMfbHpOItPXJ499dlutXi8Q3WTE4mUHK5OVl6+K4SFj7cmuEIZziRd4l+fb+XFH3eTmp5ldjwRKYJUrkREiqm5W44zadlBAEZ0qsaIzjVNTiRSMrWo6s2yUe15rPXl023nh52gx5T1bDp81uRkIlLUqFyJiBRDC7ed4OWf9gBwRyUbT3fWESsReyrj7MjEe0OZP7Qllcu7cer8RfrPCmPC4r2kZegolohcpnIlIlLMfL/9FGN/2A3AoDbB3H2bDYvFYnIqkdKhTfWKLH+mA/1b3gbAnE3H6PnhH2w7lmByMhEpClSuRESKkcURZ3huUQSGAQNaBzOuRy3Uq0RurbIujky6rz5fDW5BJS9Xjp9Lo8/Mzbz26z4uZWabHU9ETKRyJSJSTCzbHc3ohTuxGdCvRRAT7q6nI1YiJupQy4flozvQp1llDAM+33CUXh/+QfiJ82ZHExGTqFyJiBQDK/fFMnLBDrJtBg80qcwbvevj4KBiJWI2T1cn3nmwIV8MbI6fpwtHzl7gwembeHPZfh3FEimFVK5ERIq4tQfjeGpeOFk2g3sbVeKdBxuoWIkUMbfX8eX3Zzpyf5NAbAbMXHeEu6duYNepRLOjicgtpHIlIlKEbYg8y+Nzt5ORbaNXfX/ef6ghVhUrkSLJq4wTH/RpxKf/akrFsi5ExqVy3yebeO+3g6Rn6SiWSGmgciUiUkRtOXKOIV9tIyPLRpe6fnz4cGMcrfrftkhR162ePytGd+DuhpXIthlMWxPFvdM2sud0ktnRRMTO9Le0iEgR9OexBAbP2calTBudavvw8SONcVKxEik2yrs7M7VfYz55pAne7s4ciEmh98cbmbLyEJnZNrPjiYid6G9qEZEiZufJRAZ+sY20jGza1ajIjEeb4uJoNTuWiBRAr/oB/D66Az1D/cmyGUxZGUnvjzdyICbZ7GgiYgcqVyIiRcie00kM+DyM1PQsWlXzZtaAZrg6qViJFGcVy7rwySNN+KhfY8qVcWLvmWTunrqBj9dEkaWjWCIlisqViEgRsT86mUc/DyP5UhbNgsvz+WPNcXNWsRIpCSwWC/c0rMTvozvQpa4fmdkG7/52kAembyIqLsXseCJSSFSuRESKgMjYFB79LIzEtEwaBZXji0HNcXdxNDuWiBQyXw9XZg1oygd9GuLp6kjEqSR6fbSBmesOk20zzI4nIjdJ5UpExGRH4lPp/1kY5y5kEBroyZeDW+Dh6mR2LBGxE4vFwv1NKvP76I50qu1DRpaNN5cd4KEZmzgSn2p2PBG5CSpXIiImOn7uAv1nhRGfkk4dfw/mDm6Jl5uKlUhp4O/lyhcDm/POAw3wcHEk/EQiPT/8g883HMWmo1gixZLKlYiISU6dT6P/rDBiki9R07cs84a0pLy7s9mxROQWslgs9GkexPLRHWhfsyLpWTZe+3UfD3+6hePnLpgdT0TySeVKRMQE0UkX6TdrC6cTL1KtojvzhrakQlkXs2OJiEkCy7nx1eAWvHFfKGWcrWw9lkCPKX/w1eZjOoolUoyoXImI3GJxyZfoPyuMkwkXCa5QhvlDW+Hr4Wp2LBExmcVi4ZGWwfz2TAdaVfPmYmY2r/y8l0c/D+NkQprZ8UTkBqhciYjcQmdT0+n/WRhHz14gsJwb84e2wt9LxUpE/l+QdxnmD2nFxHvq4eZkZdPhc/SYsp75YScwDB3FEinKVK5ERG6R8xcyePSzMKLiUgnwcmXB0FYElnMzO5aIFEEODhYea1OFZaPa07xKeS5kZPPij7sZMHsrZxIvmh1PRK5B5UpE5BZISsvk0c/DOBCTgq+HC/OHtuK2CmXMjiUiRVyViu5883hr/ntnXVwcHfgj8izdJ6/n2z9P6iiWSBGkciUiYmfJlzIZMDuMvWeSqVjWmflDW1K1orvZsUSkmLA6WBjSvhpLR7Wn8W3lSEnP4vlFuxg8ZxuxyZfMjicif6FyJSJiR6npWQz6YhsRp5IoX8aJeUNaUcPXw+xYIlIMVfcpy6In2zC2Zx2crQ6sORhP1w/W8eOOUzqKJVJEqFyJiNhJWkYWg+dsY/vx83i5OfH1kJbU9lexEpGCszpYeLJjdZY83Y4Glb1IvpTF6IURPD53O/Ep6TnrZdsMwo4msP2shbCjCWRrOneRW8LR7AAiIiXRpcxshn71J1uPJuDh4sjcf7egXiUvs2OJSAlR08+DH4a1Yeb6I0xZeYgV+2L581gCr94biqODhVd/3Ud00iXAyleRfxLg5cr4u0PoERpgdnSREk1HrkRECll6VjZPzN3OxqhzuDtbmTO4BQ0qlzM7loiUMI5WB4bfXoPFI9oREuDJ+bRMRi7YwbB54f8rVv8vJukSw74OZ/meaJPSipQOKlciIoUoI8vG8HnhrDsUj5uTlS8GtaBpcHmzY4lICVY3wJOfR7RlZOca11znykmBE3/Zp1MERexI5UpEpJBkZtt4esEOVu6Pw8XRgc8fa0aLqt5mxxKRUsDJ6kCb6hWvu44BRCddYuvRhFsTSqQUUrkSESkEWdk2xnwbwfK9MThbHfh0QDPa1Lj+Fx0RkcIUl3Jj07JHxaXYOYlI6aUJLUREblK2zeD5Rbv4JeIMTlYL0x9tQsdaPmbHEpFSxtfD9YbWe/nnvfwSEU3P+v70CPUnwMvNzslESg+VKxGRm2CzGbz4w25+2HEaq4OFqf2acEddP7NjiUgp1KKqNwFersQkXeJaV1U5WS1kZhtsPZbA1mMJTPxlH02Dy9Mz1J+e9QMILKeiJXIzVK5ERArIMAxeWbyHhX+exMECHz7ciB6h/mbHEpFSyupgYfzdIQz7OhwL5CpYlv/9d2q/xjSoXI7le2JYujuaP4+fZ/v/fl5fsp+GQeXoFepPr/oBBHmXMeFdiBRvKlciIgVgGAav/rqPr7ecwGKB9/s05K4GlcyOJSKlXI/QAKY/2oSJv+zLNR27/9/uczW4XVUGt6tKTNIlftt7uWhtPZZAxMlEIk4m8uayA9QP9KJnfX96hQZQpaK7WW9JpFhRuRIRySfDMHhr2QG+2HgMgLfvb8B9jSubG0pE5H96hAbQNcSfzVFx/P5HGN3at6R1DV+sDpY86/p7ufJYmyo81qYKcSmX+H1vLEt3R7PlyDl2n05i9+kk3ll+kLoBnvT636mDNXzLmvCuRIoHlSsRkXz6YMUhZq4/AsAb94XSp3mQyYlERHKzOlhoWdWbc/sNWlb1vmqx+jtfD1cebRXMo62COZeazu/7LhetTYfPsT86mf3Ryby/4hC1/MrSq34AveoHUNO3LBbLP29bpLRQuRIRyYePVkUydXUUABPuDuGRlsEmJxIRKXwVyrrQr8Vt9GtxG+cvZLBi/+WitTHqLIdiUzkUG8mUlZFU93GnV/0AeoYGUDfAQ0VLSj2VKxGRGzRj3WE+WHEIgBd71WFg26omJxIRsb/y7s70aRZEn2ZBJKVlsnJ/LMv2RLP+0FkOx19g6uoopq6OokqFMvSsH8Cd9QOoV8lTRUtKJZUrEZEb8PmGo7y17AAAz3WvzeMdqpucSETk1vMq48QDTSvzQNPKpFzKZPWBOJbujmbtwXiOnUtj+trDTF97mCBvN3qFBtCzfgANK3upaEmpoXIlIvIP5m4+xmu/7gPg6TtqMvz2GiYnEhExn4erE/c2CuTeRoGkpmex5kAcy/ZEs/pAHCcTLjJz/RFmrj9CYDk3evxvevfGQeVwuIHrv0SKK5UrEZHr+GbrCV7+eS8AwzpVZ3SXmiYnEhEpesq6OHJ3w0rc3bASaRlZrDsYz9I9MazaH8vpxIt8vuEon284ir+na07Rahpc/oYm2hApTlSuRESu4fvtpxj3424A/t2uKs93r61TW0RE/kEZZ0d61r98SuClzGzWHYpn2e5oVu6PIyb5EnM2HWPOpmP4eLjQo54/Pev706KKN45WB7Oji9w0lSsRkatYHHGG5xZFYBgwoHUw/72zroqViEg+uTpZ6V7Pn+71/EnPymZD5FmW7o5hxb4Y4lPSmbvlOHO3HKeCuzPdQy/fsLhVNRUtKb5UrkRE/mbZ7mhGL9yJzYB+LYKYcHc9FSsRkZvk4mjljrp+3FHXj4ys+mw8fJZlu6P5fV8s5y5kMD/sBPPDTlC+jBPdQi4f0WpboyJOKlpSjKhciYj8xYp9sYxcsINsm8GDTSvzRu/6uvhaRKSQOTs6cHttX26v7csb2Ta2HDnH0t0x/LY3hoQLGSz88yQL/zyJl5sTXUP86PW/ouXiaDU7ush1qVyJiPzPmoNxDJ8XTpbN4N5GlXj7gQYqViIiduZkdaB9TR/a1/ThtXvrsfVoAkv3RLN8TyxnU9NZtP0Ui7afwsPFkS4hfvQM9adDLR9cnVS0pOhRuRIRATZEnuWJudvJyLbRq74/7z/UULNYiYjcYo5WB9rUqEibGhWZeE8ofx5LYNmeGJbtiSY2OZ0fd5zmxx2ncXe+fIphr/r+dKzli5uzipYUDSpXIlLqbTlyjiFfbSMjy0bXED8+fLixLqYWETGZ1cFCy2oVaFmtAq/cFcKOk+dZsuty0YpOusTiiDMsjjiDm5OVznV86Vnfn9tr++Luoq+3Yh59+kSkVPvzWAKD52zjUqaN22v7MK1/Y108LSJSxDg4WGga7E3TYG/+e2ddIk4lsmxPDEt3R3Pq/EWW7I5mye5oXJ0c6FTrctHqXMcXD1cns6NLKaNyJSKl1s6TiQz8YhtpGdm0r1mR6Y821cXSIiJFnIODhca3lafxbeUZ17MOe04ns2R3NMv2RHP8XBrL98awfG8Mzo4OdKjpQ6/6/txR1w8vNxUtsT+VKxEplfacTuJfn4eRmp5Fq2refPqvZro4WkSkmLFYLNSv7EX9yl680KM2+6KTWbb78hGtI2cvsHJ/LCv3x+JktdCuRkV61g+gW4gf5co4mx1dSiiVKxEpdfZHJ/Po52GkXMqiWXB5Pn+suS6GFhEp5iwWC/UqeVGvkhf/6VaLQ7GpLN0dzdLd0UTGpbLmYDxrDsbzooOFNjUq0ivUn271/PF2V9GSwqNyJSKlSmRsCo9+FkZiWiaNgsrxxaDmuvhZRKSEsVgs1Pb3oLa/B6O71iIyNiXnGq0DMSmsPxTP+kPxvPTTHlpV86ZX/QC6hfjj4+FidnQp5vSNQkRKjSPxqfT/LIxzFzIIDfTky8EtdLGziEgpUNPPg5p+Hjx9R02OxKfmFK29Z5LZGHWOjVHnePmnPbSoerlo9ajnj6+nq9mxpRhSuRKRUuH4uQv0nxVGfEo6dfw9+PrfLXVxs4hIKVTNpyzDb6/B8NtrcPzchcv30dodTcSpJLYcSWDLkQTGL95Ls+Dy9AwNoGd9fwK83MyOLcWEypWIlHinzqfRf1YYMcmXqOlblnlDWupiZhERIbiCO092rM6THatzMiGN3/ZePqIVfiKRbcfOs+3YeV79dR9Nbit3+YhWqD+Vy5cxO7YUYSpXIlKiRSddpN+sLZxOvEi1iu7MG9qSCmV1Tr2IiOQW5F2GIe2rMaR9Nc4kXmT5nss3LP7z+HnCTyQSfiKR15fsp2FlL3rWD6BrnYpmR5YiSOVKREqsuORL9J8VxsmEiwRXKMP8oa3w9dA59CIicn2VyrkxuF1VBrerSmzypZwjWluPJhBxKomIU0m8tQwqu1s54X6EuxpVpmpFd7NjSxGgciUiJVJ8Sjr9Zm3h6NkLVC7vxvyhrfD3UrESEZH88fN0ZUDrKgxoXYX4lHR+23v5iNbmw+c4dcHC+yujeH9lFHX8PehVP4Be9f2p4ethdmwxicqViJQ4CRcyePSzMA7HXyDAy5UFQ1sRWE4XI4uIyM3x8XDh0VbBPNoqmJjEC0z5dhWnHXzZfCSBAzEpHIhJ4YMVh6jpW/Z/RSuAWn5lsVgsZkeXW0TlSkRKlKS0TB79LIyDsSn4ergwf2grgrx18bGIiBSuCu7OtPYz6NWrKRcyDX7fF8uy3dFsiDpLZFwqH66K5MNVkVTzcafX/2YdDAnwVNEq4VSuRKTESL6UyYDZYeyLTqZiWWfmD22lc+BFRMTuypVxpk+zIPo0CyLpYiar9seydHcM6w/FcyT+AtPWRDFtTRTBFcrQMzSAO+sHEBqoolUSqVyJSImQmp7FwNlbiTiVRPkyTswb0ooavmXNjiUiIqWMl5sT9zepzP1NKpNyKZPVB+JYujuatQfjOX4ujRnrDjNj3WEql3ejV/0Aeob60yionIpWCVHgcpWYmMiiRYs4fPgwzz33HN7e3oSHh+Pn50dgYGBhZhQRua60jCwGz9lG+IlEvNyc+HpIS2r762JiERExl4erE/c2CuTeRoFcSM9izcE4lu2OYfWBOE6dv8in64/w6fojVPJypUdoAHc28KdxUHkcHFS0iqsClatdu3bRpUsXvLy8OHbsGEOHDsXb25sffviBEydO8NVXXxV2ThGRq7qUmc2QL/9k69EEPFwcmfvvFtSr5GV2LBERkVzcXRy5q0El7mpQiYsZ2aw9GMfSPTGs3h/LmaRLzN54lNkbj+Ln6ULP0MtHtJpV8caqolWsFKhcjRkzhoEDB/LOO+/g4fH//zrcq1cv+vfvX2jhRESu51JmNo/P3c6mw+dwd7by5b9b0KByObNjiYiIXJebs5We9QPoWT+AS5nZrD8Uz7I9MazcF0tscjpzNh1jzqZjVCzrQo9QP3qFBtCiqjeOVgezo8s/KFC52rZtGzNnzsyzPDAwkJiYmJsOJSLyTzKybAyfF876Q/G4OVn5YlALmtxW3uxYIiIi+eLqZKVbPX+61fMnPSubjVFnWbIrhhX7Yjibms7XW07w9ZYTVHB3pls9f3rV96dVtQo4qWgVSQUqVy4uLiQnJ+dZfujQIXx8fG46lIjI9WRm2xi5IJxVB+JwcXTg88ea0aKqt9mxREREboqLo5XOdfzoXMePjKz6bDp8lmW7Y/htXwznLmSwYOsJFmw9QbkyTnQL8aNn/QDaVq+Is6OKVlFRoHJ1zz338Oqrr/Ltt98CYLFYOHHiBC+88AIPPPBAoQYUEfmrrGwboxfu5Le9sThbHfh0QDPa1KhodiwREZFC5ezoQKfavnSq7cvr2aGEHUlgye5oft97uWh9++cpvv3zFJ6ujnQNuXxEq13Nirg4Ws2OXqoVqFy9//77PPjgg/j6+nLx4kU6duxITEwMrVu35o033ijsjCIiAGTbDJ5ftItfd0XjZLUw/dEmdKylo+UiIlKyOVkdaFezIu1qVuS1e+ux9VgCy3bHsGzP5VMHvw8/xffhp/BwceSOur70rB9Ax1o+uDqpaN1qBSpXXl5erFixgo0bNxIREUFqaipNmjShS5cuhZ1PRAQAm81g3A+7+GHHaawOFqb2a8Iddf3MjiUiInJLOVodaFO9Im2qV2TCPfXYfvw8S3dHs2xPNLHJ6fy08ww/7TxDGWcrnev4cmf9ADrV9sXNWUXrVsh3ucrMzMTNzY2dO3fStm1b2rZta49cIiI5DMPglcV7+PbPUzhY4MOHG9Ej1N/sWCIiIqayOlhoUdWbFlW9eeWuEHacPM/S3TEs2x3NmaRL/Lorml93RePmZOX2Oj70DA2gcx1f3F0KfKtb+Qf5HlknJyduu+02srOz7ZFHRCQXwzB49dd9fL3lBBYLfNCnEXc1qGR2LBERkSLFwcFC02BvmgZ789876xJxKollu6NZsjuaU+cvsnR3DEt3x+Di6ECn2j70qn+5aHm4OpkdvUQpUG196aWXePHFF5k7dy7e3pqhS0TswzAM3lp2gC82HgPg7Qca0LtxoLmhREREijiLxUKjoHI0CirH2J512HM6maV7olm6O5rj59L4bW9szsRQHWpVpGdoAF1C/PByU9G6WQWat3HatGmsX7+eSpUqUbt2bZo0aZLrJ78+/vhjqlSpgqurKy1btmTr1q3XXT8xMZHhw4cTEBCAi4sLtWrVYunSpTe1TREpej5YcYiZ648A8MZ9ofRpFmRyIhERkeLFYrFQv7IXL/Sow9pnO7H06faM7FyDaj7uZGTbWLk/jv98F0Gz11cw8IutfLvtJIlpGWbHLrYKdOSqd+/ehRZg4cKFjBkzhhkzZtCyZUumTJlC9+7dOXjwIL6+vnnWz8jIoGvXrvj6+rJo0SICAwM5fvw45cqVK/A2RaTo+WhVJFNXRwEw4e4QHmkZbHIiERGR4s1isRBSyZOQSp6M6VqLQ7GpOZNhHIpNZe3BeNYejOfFHy20rl6BXvUD6BbiR4WyLmZHLzYKVK7Gjx9faAE++OADhg4dyqBBgwCYMWMGS5YsYfbs2YwdOzbP+rNnzyYhIYFNmzbh5HT50GWVKlVuapsiUrRMX3uYD1YcAuClXnUZ2LaqyYlERERKFovFQm1/D2r7ezC6ay2i4lJYtjuGJbujORCTwh+RZ/kj8iwv/bibVtUq0LN+AD3q+ePjoaJ1PTc1Vcj27dvZv38/APXq1aNx48b5en1GRgbbt29n3LhxOcscHBzo0qULmzdvvuprFi9eTOvWrRk+fDg///wzPj4+9O/fnxdeeAGr1Vqgbaanp5Oenp7zODk5Gbg8M2JmZma+3lNhu7J/s3OUVBpf+8vvGH+x6ThvLz8IwJguNRjYOki/n+vQZ9i+NL72pfG1L42vfZW08Q0u78qTHarwZIcqHD17gd/2xrJ8Xyx7z6Sw6fA5Nh0+xys/76F5cHl61POjW4gvfp6udstTlMY3PxkshmEY+d1BXFwcDz/8MGvXrs05HS8xMZHbb7+db775Bh+fG7up55kzZwgMDGTTpk20bt06Z/nzzz/PunXrCAsLy/OaOnXqcOzYMR555BGeeuopoqKieOqpp3j66acZP358gbY5YcIEJk6cmGf5/PnzKVOmzA29FxG5eX/EWFh09PJ9OHpUttEzyGZyIhERkdLt7CWIOGchIsGB46mWnOUWDKp4QKMKNhp6G5QvwQe00tLS6N+/P0lJSXh6el533QIduRo5ciQpKSns3buXunXrArBv3z4ee+wxnn76aRYsWFCQzd4Qm82Gr68vn376KVarlaZNm3L69GnefffdAp+uOG7cOMaMGZPzODk5maCgILp16/aPA2hvmZmZrFixgq5du+acBimFR+Nrfzc6xgv/PMWizfsAeKJ9Vf7TtQYWi+Wa68tl+gzbl8bXvjS+9qXxta/SOL6nEy9ePqK1N5YdJ5M4mgJHU6z8eAwaBXnRo54f3UP8qFze7ab3VZTG98pZbTeiQOVq+fLlrFy5MqdYAYSEhPDxxx/TrVu3G95OxYoVsVqtxMbG5loeGxuLv//VbxAaEBCAk5MTVuv/32W6bt26xMTEkJGRUaBturi44OKSt247OTmZ/su8oihlKYk0vvZ3vTFetP0ULy++XKyGtKvK2F51VazySZ9h+9L42pfG1740vvZVmsa3io8TT3Ty5IlONYlOusjyPTEs2x3DtuMJ7DyZxM6TSby1/BANKnvRMzSAXvX9Ca7gflP7LArjm5/9F2gqdpvNdtWdODk5YbPd+Gk8zs7ONG3alFWrVuXa9qpVq3Kd0vdXbdu2JSoqKtd+Dh06REBAAM7OzgXapoiY5+edp3l+UQSGAY+1DualO1WsREREiroALzcGta3Kt0+2Zsu4O3j13nq0quaNgwV2nUri7eUH6PjuWu786A8+XhPFkfhUsyPfEgUqV507d2bUqFGcOXMmZ9np06cZPXo0d9xxR762NWbMGGbNmsWXX37J/v37GTZsGBcuXMiZ6W/AgAG5JqcYNmwYCQkJjBo1ikOHDrFkyRImTZrE8OHDb3ibIlI0LNsdzZhvI7AZ0K/FbYy/u56KlYiISDHj5+nKgNZV+Obx1oS92IU37gulXY2KWB0s7D2TzLu/HaTz++voMWU9H66MJDI25brby7YZhB1NYPtZC2FHE8i25XuKCNMU6LTAadOmcc8991ClShWCgi7f1PPkyZOEhoby9ddf52tbffv2JT4+nldeeYWYmBgaNWrE8uXL8fPzA+DEiRM4OPx/BwwKCuK3335j9OjRNGjQgMDAQEaNGsULL7xww9sUEfOt2BfLyAU7yLYZPNi0Mm/0DsXBQcVKRESkOPPxcOGRlsE80jKYhAsZrNgXw5LdMWyKOsuBmBQOxKQweeUhaviWpVf9y6cO1vbzyPnH1eV7opn4yz6iky4BVr6K/JMAL1fG3x1Cj9AAc9/cDShQuQoKCiI8PJyVK1dy4MAB4PJ1T126dClQiBEjRjBixIirPrd27do8y1q3bs2WLVsKvE0RMdeag3E8NW87WTaDextV4u0HGqhYiYiIlDDe7s70bX4bfZvfRmJaBiv2xbJsTwx/RMYTFZfKR6si+WhVJNUqutOzvj9erk68uewAfz9OFZN0iWFfhzP90SZFvmAV+D5XFouFrl270rVr18LMIyIl3IbIszwxdzuZ2Qa96vvz/kMNsapYiYiIlGjlyjjzULMgHmoWRNLFTFbtj2Xp7hjWR8Zz5OwFPl5z+JqvNQALMPGXfXQN8S/S3xsKVK6efvppatSowdNPP51r+bRp04iKimLKlCmFkU1Eirm/njNd4WgCDg5Whny1jYwsG11D/Pjw4cY4Wgt06aeIiIgUU15uTtzfpDL3N6lMyqVMVh+I4+stx9l27Pw1X2MA0UmX2Ho0gdbVK9y6sPlUoHL1/fffs3jx4jzL27Rpw1tvvaVyJSJXPWfawuX/Od5e24dp/RvjpGIlIiJSqnm4OnFvo0CA65arK+JSLtk70k0pULk6d+4cXl5eeZZ7enpy9uzZmw4lIsXb8j3RDPs6PM8501ce39c4EBdH699fJiIiIqWUr4droa5nlgL9s3GNGjVYvnx5nuXLli2jWrVqNx1KRIqvbJvBxF/25SlWV1iAN5cdKFbTqoqIiIh9tajqTYCXK9e6msoCBHi50qKq962MlW8FOnI1ZswYRowYQXx8PJ07dwZg1apVvP/++zolUKSU23o04X+nAl5dcTlnWkRERG4dq4OF8XeHMOzr8JzLCK64UrjG3x1SpCezgAKWq8GDB5Oens4bb7zBa6+9BkCVKlWYPn06AwYMKNSAIlK83Oi50EX9nGkRERG5tXqEBjD90SZ/uWb7Mv+Sfp8rgGHDhjFs2DDi4+Nxc3OjbNmyhZlLRIqpknLOtIiIiNx6PUID6Briz+aoOH7/I4xu7VvSuoZvkT9idcVNT9Xl4+PD9u3bWbZsGefP//MMHyJSsrWo6k05N6drPl9czpkWERERc1gdLLSs6k3TigYtq3oXm2IF+Txy9fbbb5OamppzKqBhGPTs2ZPff/8dAF9fX1atWkW9evUKP6mIFAsnEtK4mJl91eeK0znTIiIiIvmVryNXCxcuJDQ0NOfxokWLWL9+PX/88Qdnz56lWbNmTJw4sdBDikjxkJ6VzcgF4aRn2ajhWxZ/z9yn/vl7uTL90SbF4pxpERERkfzK15Gro0eP0qBBg5zHS5cu5cEHH6Rt27YA/Pe//+Whhx4q3IQiUmy8tewAe04nU76ME3P/3QJfD9die860iIiISH7l68hVVlYWLi4uOY83b95MmzZtch5XqlRJNxEWKaVW7Ivli43HAHjvoYYEeLkV63OmRURERPIrX+WqevXqrF+/HoATJ05w6NAhOnTokPP8qVOnqFBB960RKW3OJF7kuUURAPy7XVXuqOtnciIRERGRWy9fpwUOHz6cESNG8Mcff7BlyxZat25NSEhIzvOrV6+mcePGhR5SRIqurGwbz3yzk8S0TOoHevF8j9pmRxIRERExRb7K1dChQ7Farfzyyy906NCB8ePH53r+zJkzDB48uFADikjR9tGqSLYeS6CsiyNT+zXGxdFqdiQRERERU+T7JsKDBw++ZoH65JNPcj1+6623ePLJJylXrlyBwolI0bbp8FmmrokC4I37QqlS0d3kRCIiIiLmuembCF/PpEmTSEhIsOcuRMQkZ1PTeeabnRgG9G0WxL2NAs2OJCIiImIqu5YrwzDsuXkRMYnNZvDsdxHEpaRTw7cs4+8J+ecXiYiIiJRwdi1XIlIyfbbhCGsPxuPi6MC0/o0p45zvM4xFREREShyVKxHJl50nE3ln+UEAXrk7hDr+niYnEhERESkaVK5E5IYlX8pk5IJwsmwGver707/FbWZHEhERESkyVK5E5IYYhsG473dzMuEilcu78eb9DbBYLGbHEhERESky7Fqu2rdvj5ubmz13ISK3yIKtJ1myOxpHBwtT+zXGy83J7EgiIiIiRUqBy9Xhw4f573//S79+/YiLiwNg2bJl7N27N2edpUuXEhAQcPMpRcRUB2NSmPjL5T/bz3WvTePbypucSERERKToKVC5WrduHfXr1ycsLIwffviB1NRUACIiIhg/fnyhBhQRc13MyGbE/HDSs2x0qOXD0PbVzI4kIiIiUiQVqFyNHTuW119/nRUrVuDs7JyzvHPnzmzZsqXQwomI+Sb+spfIuFR8PFz4oE9DHBx0nZWIiIjI1RSoXO3evZv77rsvz3JfX1/Onj1706FEpGhYHHGGb7adxGKBKX0bUbGsi9mRRERERIqsApWrcuXKER0dnWf5jh07CAwMvOlQImK+4+cu8OIPuwEYcXsN2taoaHIiERERkaKtQOXq4Ycf5oUXXiAmJgaLxYLNZmPjxo08++yzDBgwoLAzisgtlpFlY+SCHaSmZ9G8SnlG3VHT7EgiIiIiRV6BytWkSZOoU6cOQUFBpKamEhISQocOHWjTpg3//e9/CzujiNxi7yw/wK5TSXi5OfHhw41xtOqWeCIiIiL/xLEgL3J2dmbWrFm88sor7N69m9TUVBo3bkzNmvrXbZHibvWBWD7bcBSA9x5qSKVyulediIiIyI0oULm6IigoiKCgoMLKIiImi0m6xH++jQBgYJsqdA3xMzmRiIiISPFRoHN9HnjgAd5+++08y9955x0eeuihmw4lIrdets1g1Dc7OJ+WSb1KnozrVcfsSCIiIiLFSoHK1fr16+nVq1ee5T179mT9+vU3HUpEbr2pqyMJO5qAu7OVaf2b4OJoNTuSiIiISLFSoHKVmpqa6+bBVzg5OZGcnHzToUTk1tpy5BwfrYoE4PX7Qqla0d3kRCIiIiLFT4HKVf369Vm4cGGe5d988w0hISE3HUpEbp2ECxmM+mYHNgMebFqZ+xpXNjuSiIiISLFUoAktXn75Ze6//34OHz5M586dAVi1ahULFizgu+++K9SAImI/hmHw7HcRxCanU83HnYn31DM7koiIiEixVaBydffdd/PTTz8xadIkFi1ahJubGw0aNGDlypV07NixsDOKiJ18vuEoqw/E4ezowLR+TXB3uakJREVERERKtQJ/k7rzzju58847CzOLiNxCu04l8vbyAwC8fGddQip5mpxIREREpHi7qX+mzsjIIC4uDpvNlmv5bbfddlOhRMS+Ui5lMnLBDjKzDXrU8+fRVsFmRxIREREp9gpUriIjIxk8eDCbNm3KtdwwDCwWC9nZ2YUSTkQKn2EYvPjjHo6fSyOwnBtvP9AAi8VidiwRERGRYq9A5WrgwIE4Ojry66+/EhAQoC9mIsXIt3+e5JeIM1gdLHzUrxFeZZzMjiQiIiJSIhSoXO3cuZPt27dTp06dws4jInYUGZvC+MV7AfhPt1o0DfY2OZGIiIhIyVGg+1yFhIRw9uzZws4iInZ0KTObEfN3cCnTRvuaFXmyQ3WzI4mIiIiUKAUqV2+//TbPP/88a9eu5dy5cyQnJ+f6EZGi59Vf93EwNoWKZV34oE8jHBx0Oq+IiIhIYSrQaYFdunQB4I477si1XBNaiBRNS3ZFMz/sBBYLTO7bEB8PF7MjiYiIiJQ4BSpXa9asKewcImInJxPSGPv9LgCGdaxO+5o+JicSERERKZkKVK46duxY2DlExA4ysmyMWLCDlPQsmtxWjtFda5kdSURERKTEKtA1VwB//PEHjz76KG3atOH06dMAzJ07lw0bNhRaOBG5Oe//fpCIk4l4ujryUb/GOFkL/EdeRERERP5Bgb5pff/993Tv3h03NzfCw8NJT08HICkpiUmTJhVqQBEpmLUH45i5/ggA7zzYgMrly5icSERERKRkK1C5ev3115kxYwazZs3Cyen/b0Datm1bwsPDCy2ciBRMbPIl/vNtBAADWgfTIzTA5EQiIiIiJV+BytXBgwfp0KFDnuVeXl4kJibebCYRuQnZNoPRC3dy7kIGdQM8ebFXXbMjiYiIiJQKBSpX/v7+REVF5Vm+YcMGqlWrdtOhRKTgPlkTxabD5yjjbGVa/8a4OlnNjiQiIiJSKhSoXA0dOpRRo0YRFhaGxWLhzJkzzJs3j2effZZhw4YVdkYRuUFbjyYweeUhAF69N5TqPmVNTiQiIiJSehRoKvaxY8dis9m44447SEtLo0OHDri4uPDss88ycuTIws4oIjfg/IUMRn2zA5sB9zcO5MGmlc2OJCIiIlKq5LtcZWdns3HjRoYPH85zzz1HVFQUqamphISEULas/pVcxAyGYfDcol1EJ12iakV3Xu0danYkERERkVIn3+XKarXSrVs39u/fT7ly5QgJCbFHLhHJhzmbjrFyfyzOVgem9mtMWZcCHZQWERERkZtQoGuuQkNDOXLkSGFnEZEC2HM6iTeXHgDgxV51CA30MjmRiIiISOlU4PtcPfvss/z6669ER0eTnJyc60dEbo3U9CxGzA8nI9tG1xA/HmtTxexIIiIiIqVWgc4d6tWrFwD33HMPFoslZ7lhGFgsFrKzswsnnYhck2EY/PfH3Rw7l0YlL1fefbBBrj+PIiIiInJrFahcrVmzprBziEg+Ldp+ip92nsHqYOGjfo0pV8bZ7EgiIiIipVqBylXHjh0LO4eI5ENUXCqv/LwXgNFdatKsirfJiURERESkQNdcAfzxxx88+uijtGnThtOnTwMwd+5cNmzYUGjhRCSvS5nZjJgfzsXMbNpUr8CwTjXMjiQiIiIiFLBcff/993Tv3h03NzfCw8NJT08HICkpiUmTJhVqQBHJ7Y0l+zkQk0IFd2em9G2E1UHXWYmIiIgUBQWeLXDGjBnMmjULJyennOVt27YlPDy80MKJSG7Ldkczd8txAN7v0xBfT1eTE4mIiIjIFQUqVwcPHqRDhw55lnt5eZGYmHizmUTkKk4mpPH897sAeKJjNTrV9jU5kYiIiIj8VYHKlb+/P1FRUXmWb9iwgWrVqt10KBHJLTPbxtPf7CDlUhaNgsrxbLfaZkcSERERkb8pULkaOnQoo0aNIiwsDIvFwpkzZ5g3bx7PPvssw4YNK+yMIqXeBysOseNEIh6ujkzt1xgna4HnohEREREROynQVOxjx47FZrNxxx13kJaWRocOHXBxceHZZ59l5MiRhZ1RpFRbfyie6WsPA/D2Aw0I8i5jciIRERERuZoClSuLxcJLL73Ec889R1RUFKmpqYSEhFC2bNnCzidSqsWlXGLMtzsBeKTlbfSqH2BuIBERERG5pgKVqyucnZ0JCQkprCwi8hc2m8GYhRGcTc2gjr8HL9+lP2siIiIiRVmBytWFCxd46623WLVqFXFxcdhstlzPHzlypFDCiZRm09cdZkPUWdycrEzr3xhXJ6vZkURERETkOgpUroYMGcK6dev417/+RUBAABaLbmIqUpi2H0/ggxWHAJh4Tz1q+HqYnEhERERE/kmBytWyZctYsmQJbdu2Lew8IqVeYloGTy/YSbbN4N5GlXioWWWzI4mIiIjIDSjQfM7ly5fH29u7sLOIlHqGYfDC97s4nXiR4ApleL13qI4Mi4iIiBQTBSpXr732Gq+88gppaWmFEuLjjz+mSpUquLq60rJlS7Zu3XrNdefMmYPFYsn14+rqmmud1NRURowYQeXKlXFzcyMkJIQZM2YUSlYRe5q75Ti/7Y3FyWphWr8meLg6mR1JRERERG7QDZ8W2Lhx41z/gh4VFYWfnx9VqlTBySn3F8Dw8PAbDrBw4ULGjBnDjBkzaNmyJVOmTKF79+4cPHgQX1/fq77G09OTgwcP5jz++7/sjxkzhtWrV/P1119TpUoVfv/9d5566ikqVarEPffcc8PZRG6lvWeSeP3X/QCM7VmX+pW9TE4kIiIiIvlxw+Wqd+/edgnwwQcfMHToUAYNGgTAjBkzWLJkCbNnz2bs2LFXfY3FYsHf3/+a29y0aROPPfYYnTp1AuDxxx9n5syZbN26VeVKiqQL6VmMnL+DjGwbd9TxZXDbKmZHEhEREZF8uuFyNX78+ELfeUZGBtu3b2fcuHE5yxwcHOjSpQubN2++5utSU1MJDg7GZrPRpEkTJk2aRL169XKeb9OmDYsXL2bw4MFUqlSJtWvXcujQISZPnnzV7aWnp5Oenp7zODk5GYDMzEwyMzNv9m3elCv7NztHSVVUxve/P+3hyNkL+Hm6MKl3CFlZWabmKUxFZYxLKo2vfWl87Uvja18aX/vS+NpXURrf/GSwGIZhFHRH27dvZ//+y6cx1atXj8aNG+fr9WfOnCEwMJBNmzbRunXrnOXPP/8869atIywsLM9rNm/eTGRkJA0aNCApKYn33nuP9evXs3fvXipXvjyrWnp6Oo8//jhfffUVjo6OODg4MGvWLAYMGHDVHBMmTGDixIl5ls+fP58yZcrk6z2J5Ne2eAtfR1mxYDCiXjY1PM1OJCIiIiJXpKWl0b9/f5KSkvD0vP4XtQJNxR4XF8fDDz/M2rVrKVeuHACJiYncfvvtfPPNN/j4+BRkszekdevWuYpYmzZtqFu3LjNnzuS1114DYOrUqWzZsoXFixcTHBzM+vXrGT58OJUqVaJLly55tjlu3DjGjBmT8zg5OZmgoCC6dev2jwNob5mZmaxYsYKuXbvmubZNbp7Z43v07AXGTd8CZDOycw1G3l79lmewN7PHuKTT+NqXxte+NL72pfG1L42vfRWl8b1yVtuNKFC5GjlyJCkpKezdu5e6desCsG/fPh577DGefvppFixYcEPbqVixIlarldjY2FzLY2Njr3tN1V85OTnRuHFjoqKiALh48SIvvvgiP/74I3feeScADRo0YOfOnbz33ntXLVcuLi64uLhcddtm/zKvKEpZSiIzxvdSZjbPfLubtIxsWlXzZlSX2lgdSu606/oM25fG1740vval8bUvja99aXztqyiMb372X6Cp2JcvX84nn3ySU6wAQkJC+Pjjj1m2bNkNb8fZ2ZmmTZuyatWqnGU2m41Vq1blOjp1PdnZ2ezevZuAgADg/6+TcnDI/dasVis2m+2Gs4nY21vLDrAvOhlvd2c+fLhxiS5WIiIiIqVBgY5c2Wy2qzY4JyenfBeYMWPG8Nhjj9GsWTNatGjBlClTuHDhQs7sgQMGDCAwMJA333wTgFdffZVWrVpRo0YNEhMTeffddzl+/DhDhgwBLk/T3rFjR5577jnc3NwIDg5m3bp1fPXVV3zwwQcFebsihe63vTHM2XQMgPcfaoifp+v1XyAiIiIiRV6BylXnzp0ZNWoUCxYsoFKlSgCcPn2a0aNHc8cdd+RrW3379iU+Pp5XXnmFmJgYGjVqxPLly/Hz8wPgxIkTuY5CnT9/nqFDhxITE0P58uVp2rQpmzZtIiQkJGedb775hnHjxvHII4+QkJBAcHAwb7zxBk8++WRB3q5IoTqdeJHnF+0CYGj7qtxe5+r3cxMRERGR4qVA5WratGncc889VKlShaCgIABOnjxJaGgoX3/9db63N2LECEaMGHHV59auXZvr8eTJk685pfoV/v7+fPHFF/nOIWJvWdk2Ri3YQdLFTBpW9uK57nXMjiQiIiIihaRA5SooKIjw8HBWrlzJgQMHAKhbt+5VJ4sQkf83ZWUkfx4/j4eLI1P7NcHZsUCXPYqIiIhIEZSvb3arV68mJCSE5ORkLBYLXbt2ZeTIkYwcOZLmzZtTr149/vjjD3tlFSnWNkSe5eO1l2e1nHR/fW6roHuoiYiIiJQk+SpXU6ZMYejQoVe995OXlxdPPPGEJo0QuYr4lHRGf7sTw4B+LYK4u2ElsyOJiIiISCHLV7mKiIigR48e13y+W7dubN++/aZDiZQkNpvBmG93Ep+STi2/srxyVz2zI4mIiIiIHeSrXMXGxl73JlqOjo7Ex8ffdCiRkuTTP47wR+RZXJ0cmNa/CW7OVrMjiYiIiIgd5KtcBQYGsmfPnms+v2vXrpyb+YoIhJ84z3u/HQRg/N31qOXnYXIiEREREbGXfJWrXr168fLLL3Pp0qU8z128eJHx48dz1113FVo4keIs6WImTy/YQZbN4K4GATzcPMjsSCIiIiJiR/maiv2///0vP/zwA7Vq1WLEiBHUrl0bgAMHDvDxxx+TnZ3NSy+9ZJegIsWJYRiM/X4Xp85f5DbvMky6vz4Wi8XsWCIiIiJiR/kqV35+fmzatIlhw4Yxbtw4DMMAwGKx0L17dz7++GP8/PzsElSkOJkXdoJle2JwdLAwtV9jPF2vfa2iiIiIiJQM+b6JcHBwMEuXLuX8+fNERUVhGAY1a9akfPny9sgnUuzsj07m1V/3AfBCjzo0DCpnbiARERERuSXyXa6uKF++PM2bNy/MLCLFXlpGFiPmh5ORZeP22j78u11VsyOJiIiIyC2SrwktROT6Jizey+H4C/h5uvDeQw1xcNB1ViIiIiKlhcqVSCH5eedpvv3zFBYLTOnbmAplXcyOJCIiIiK3kMqVSCE4dvYCL/6wG4CRnWvSunoFkxOJiIiIyK2mciVyk9KzshmxIJwLGdm0qOrN051rmB1JREREREygciVyk95edpA9p5MpX8aJDx9uhKNVf6xERERESiN9CxS5CSv3xTJ741EA3nuoIQFebiYnEhERERGzqFyJFFB00kWeXRQBwOC2Vbmjrm6gLSIiIlKaqVyJFEBWto1RC3aSmJZJaKAnL/SsbXYkERERETGZypVIAXy0OoqtxxJwd7YyrV8TXBytZkcSEREREZOpXInk06bDZ5m6OhKASffXp0pFd5MTiYiIiEhRoHIlkg/nUtN55pudGAb0aVaZexsFmh1JRERERIoIlSuRG2SzGfznuwjiUtKp4VuWCffUMzuSiIiIiBQhKlciN+jzDUdZezAeZ0cHpvVvTBlnR7MjiYiIiEgRonIlcgN2nkzk7eUHAHjlrhDq+HuanEhEREREihqVK5F/kHwpk5ELwsmyGfSq788jLW8zO5KIiIiIFEEqVyLXYRgG437YzcmEi1Qu78ab9zfAYrGYHUtEREREiiCVK5Hr+GbbSZbsisbRwcJH/Rrj5eZkdiQRERERKaJUrkSu4VBsChMW7wXg2e61aXJbeZMTiYiIiEhRpnIlchUXM7IZPi+c9CwbHWr58Hj7amZHEhEREZEiTuVK5Cpe/XUvkXGp+Hi48EGfhjg46DorEREREbk+lSuRv/kl4gwLtp7EYoEpfRtRsayL2ZFEREREpBhQuRL5ixPn0hj3w24AhneqQdsaFU1OJCIiIiLFhcqVyP9kZNkYsSCc1PQsmgWX55kuNc2OJCIiIiLFiMqVyP+8+9sBdp1KwsvNiQ/7NcbRqj8eIiIiInLj9O1RBFhzMJ5ZfxwF4N0HGxBYzs3kRCIiIiJS3KhcSamXmA4v/LAHgIFtqtCtnr/JiURERESkOFK5klIt22YwN8qB82mZ1KvkybhedcyOJCIiIiLFlMqVlGqfrDtCVLIDZZytTO3XGBdHq9mRRERERKSYUrmSUmvLkXNMW3MYgFfvrks1n7ImJxIRERGR4kzlSkqlhAsZPPPNTmwGtPCxcW+jSmZHEhEREZFiTuVKSh3DMHjuuwhiki9RrWIZHqxqMzuSiIiIiJQAKldS6szeeIxVB+JwdnRgcp8GuOgyKxEREREpBCpXUqrsOpXIW8v2A/DfO+sSEuBpciIRERERKSlUrqTUSLmUycgFO8jMNuhez49/tQo2O5KIiIiIlCAqV1IqGIbBSz/u4fi5NALLufHOAw2xWCxmxxIRERGREkTlSkqF7/48xeKIM1gdLHzUrxFeZZzMjiQiIiIiJYzKlZR4UXEpvLJ4DwBjutaiabC3yYlEREREpCRSuZIS7VJmNsPn7eBSpo12NSoyrGN1syOJiIiISAmlciUl2mu/7uNgbAoVyzrzQd+GODjoOisRERERsQ+VKymxluyKZl7YCQAm922Er4eryYlEREREpCRTuZIS6WRCGmN/2AXAsE7VaV/Tx+REIiIiIlLSqVxJiZOZbWPkgh2kXMqiyW3lGNO1ltmRRERERKQUULmSEue93w+y82Qinq6OfNSvMU5WfcxFRERExP70rVNKlLUH45i57ggA7zzYgMrly5icSERERERKC5UrKTHiki/xn28jAPhXq2B6hAaYnEhEREREShOVKykRsm0GzyzcybkLGdTx9+ClO+uaHUlEREREShmVKykRpq+NYtPhc7g5WZnWvwmuTlazI4mIiIhIKaNyJcXetmMJfLDiEACv9Q6lhm9ZkxOJiIiISGmkciXFWmJaBk8v2IHNgPsaB/JAk0CzI4mIiIhIKaVyJcWWYRg8+90uopMuUbWiO6/1DsVisZgdS0RERERKKZUrKba+3HSMlftjcbY6MLVfY8q6OJodSURERERKMZUrKZb2nE5i0tIDAIzrVYfQQC+TE4mIiIhIaadyJcVOanoWIxfsICPbRpe6fgxsU8XsSCIiIiIiKldSvBiGwcs/7eHo2QsEeLny7oMNdJ2ViIiIiBQJKldSrHwffpofd5zGwQIf9WtMeXdnsyOJiIiIiAAqV1KMRMWl8vJPewAY3aUWzat4m5xIREREROT/qVxJsXApM5sR88O5mJlNm+oVeOr2GmZHEhERERHJReVKioVJS/dzICaFCu7OTOnbCKuDrrMSERERkaJF5UqKvOV7ovlq83EA3u/TEF9PV5MTiYiIiIjkpXIlRdqp82k8v2gXAE90qEan2r4mJxIRERERuTqVKymyMrNtPL1gB8mXsmgUVI5nu9c2O5KIiIiIyDWpXEmRNXnFIcJPJOLh6sjUfo1xsurjKiIiIiJFl76tSpH0R2Q809cdBuCt+xsQ5F3G5EQiIiIiItenciVFTlzKJUYv3IlhQP+Wt3FngwCzI4mIiIiI/COVKylSbDaD/3wbwdnUDGr7efDKXSFmRxIRERERuSFFolx9/PHHVKlSBVdXV1q2bMnWrVuvue6cOXOwWCy5flxd807NvX//fu655x68vLxwd3enefPmnDhxwp5vQwrBjPWH+SPyLK5ODkzr3xhXJ6vZkUREREREbojp5WrhwoWMGTOG8ePHEx4eTsOGDenevTtxcXHXfI2npyfR0dE5P8ePH8/1/OHDh2nXrh116tRh7dq17Nq1i5dffvmqJUyKju3HE3j/90MAvHpPKDX9PExOJCIiIiJy4xzNDvDBBx8wdOhQBg0aBMCMGTNYsmQJs2fPZuzYsVd9jcViwd/f/5rbfOmll+jVqxfvvPNOzrLq1atfc/309HTS09NzHicnJwOQmZlJZmZmvt5PYbuyf7Nz2FvSxUxGzt9Bts3grvr+9G7od0vec2kZXzNpjO1L42tfGl/70vjal8bXvjS+9lWUxjc/GSyGYRh2zHJdGRkZlClThkWLFtG7d++c5Y899hiJiYn8/PPPeV4zZ84chgwZQmBgIDabjSZNmjBp0iTq1asHgM1mw8vLi+eff54NGzawY8cOqlatyrhx43Lt468mTJjAxIkT8yyfP38+Zcpoljp7MwyYfciBXQkOVHQxeK5BNq6m134REREREUhLS6N///4kJSXh6el53XVNLVdnzpwhMDCQTZs20bp165zlzz//POvWrSMsLCzPazZv3kxkZCQNGjQgKSmJ9957j/Xr17N3714qV65MTEwMAQEBlClThtdff53bb7+d5cuX8+KLL7JmzRo6duyYZ5tXO3IVFBTE2bNn/3EA7S0zM5MVK1bQtWtXnJycTM1iL/PCTjDh1wM4WS0sHNqC+oFet2zfpWF8zaYxti+Nr31pfO1L42tfGl/70vjaV1Ea3+TkZCpWrHhD5arYHR9o3bp1riLWpk0b6taty8yZM3nttdew2WwA3HvvvYwePRqARo0asWnTJmbMmHHVcuXi4oKLi0ue5U5OTqb/Mq8oSlkK094zSUxafvk6q7E969KkSkVTcpTU8S1KNMb2pfG1L42vfWl87Uvja18aX/sqCuObn/2bOqFFxYoVsVqtxMbG5loeGxt73Wuq/srJyYnGjRsTFRWVs01HR0dCQnJP4V23bl3NFljEXEjPYuSCHWRk2bijji+D21YxO5KIiIiISIGZWq6cnZ1p2rQpq1atyllms9lYtWpVrqNT15Odnc3u3bsJCAjI2Wbz5s05ePBgrvUOHTpEcHBw4YWXm/bKz3s5En8Bf09X3n2oIRaLxexIIiIiIiIFZvppgWPGjOGxxx6jWbNmtGjRgilTpnDhwoWc2QMHDBhAYGAgb775JgCvvvoqrVq1okaNGiQmJvLuu+9y/PhxhgwZkrPN5557jr59+9KhQ4eca65++eUX1q5da8ZblKv4IfwU34efwsECUx5uhLe7s9mRRERERERuiunlqm/fvsTHx/PKK68QExNDo0aNWL58OX5+fgCcOHECB4f/P8B2/vx5hg4dSkxMDOXLl6dp06Zs2rQp12mA9913HzNmzODNN9/k6aefpnbt2nz//fe0a9fulr8/yetIfCr//WkPAE/fUZNW1SqYnEhERERE5OaZXq4ARowYwYgRI6763N+PNk2ePJnJkyf/4zYHDx7M4MGDCyOeFKL0rGxGzN9BWkY2Lat6M7JzTbMjiYiIiIgUClOvuZLS582lB9gXnYy3uzMfPtwYq4OusxIRERGRkkHlSm6Z3/fGMGfTMQDee6gB/l6u5gYSERERESlEKldyS5xJvMhzi3YBMKRdVTrX8TM5kYiIiIhI4VK5ErvLyrbx9IIdJF3MpEFlL57vUcfsSCIiIiIihU7lSuzuw1WR/Hn8PGVdHJnarzHOjvrYiYiIiEjJo2+5Ylcbo84ybU0UAG/eX5/gCu4mJxIRERERsQ+VK7Gbs6npPLNwJ4YBDzcP4u6GlcyOJCIiIiJiNypXYhc2m8GYbyOIT0mnpm9Zxt9dz+xIIiIiIiJ2pXIldjHrjyOsPxSPi6MD0/o3wc3ZanYkERERERG7UrmSQhd+4jzv/nYQgAn31KO2v4fJiURERERE7E/lSgpV0sVMnl6wgyybwZ0NAni4eZDZkUREREREbgmVKyk0hmEw7oddnDp/kSBvN968vz4Wi8XsWCIiIiIit4TKlRSa+VtPsHR3DI4OFqb2a4Knq5PZkUREREREbhmVKykUB2KSefWXfQC80KMOjYLKmRtIREREROQWU7mSm5aWkcWI+TtIz7LRqbYP/25X1exIIiIiIiK3nMqV3LQJi/cSFZeKr4cL7z/UEAcHXWclIiIiIqWPypXclJ93nubbP09hscCUhxtRoayL2ZFEREREREyhciUFduzsBV78YTcAI2+vQZvqFU1OJCIiIiJiHpUrKZD0rGxGLAjnQkY2Lap48/QdNc2OJCIiIiJiKpUrKZC3lx1kz+lkypVx4sN+jXC06qMkIiIiIqWbvhFLvq3cF8vsjUcBeO/BhgR4uZmcSERERETEfCpXki/RSRd5dlEEAIPaVqFLiJ/JiUREREREigaVK7lhWdk2Ri3YSWJaJqGBnoztWcfsSCIiIiIiRYbKldywqauj2HosAXdnK1P7NcHF0Wp2JBERERGRIkPlSm7I5sPnmLo6EoBJ99enakV3kxOJiIiIiBQtKlfyj86lpvPMwh3YDHioaWXubRRodiQRERERkSJH5Uquy2YzePa7CGKT06nu487Ee+uZHUlEREREpEhSuZLrmr3xKGsOxuPs6MC0/k0o4+xodiQRERERkSJJ5UquKeJkIm8vPwDAK3eFUDfA0+REIiIiIiJFl8qVXFXypUxGLthBZrZBz1B/Hml5m9mRRERERESKNJUrycMwDF78YTcnEtIILOfG/7V359FRFfbfxz+TfYAkLEIWsrAEQwgECFEMKHpKhCJyoC4ERRpEfWoNQmRpwRbZCUTFsMiiR6EuKNg+gNhCSKNGbdkEomwCqSyRvQJJAEPCzH3+6MP0Nz9AEWe8k5v365z7x9y5M/OZb3JgPnOXzLw/RTabzexYAAAAgE+jXOEKy7eU6YMvjynAz6Z5D3dWuD3Q7EgAAACAz6Ncwc2+E5WatGaXJGlM70SlxjUyOREAAABQO1Cu4PJdtUPDl21TVY1TPW5uqv9zRyuzIwEAAAC1BuUKLlM+2KV9J86paWiwZg/sKD8/zrMCAAAArhflCpKkNV8c1Tuby2SzSfmZnXRTg2CzIwEAAAC1CuUKOvztBT37f3dIkrLvSlD3hJtMTgQAAADUPpSrOq76klNPv7NNlRcvKS2+kXIy2pgdCQAAAKiVKFd13PMFX+mLb8oVbg/UnIc6K8CfXwkAAADgRvBJug776KuTevXTA5KkvAdS1Lyh3eREAAAAQO1FuaqjjpdXafR7X0iSstLj1Ts50uREAAAAQO1GuaqDHE5DOcu36/T5arWLCtP4e5LMjgQAAADUepSrOmj+h6Xa+PVp1Qvy1/yHOysk0N/sSAAAAECtR7mqYzZ9/a3mFO2TJE0b0F6tmjYwOREAAABgDZSrOuTM+WqNfLdETkO6L7W57kuNMTsSAAAAYBmUqzrCMAyNee8LHa+oUqub6mtq//ZmRwIAAAAshXJVRyz5x0EVfXVSQQF+mvdwZ9UPDjA7EgAAAGAplKs6YMc35cpdu0eS9Me+SUqODjc5EQAAAGA9lCuLq6yq0fB3tqnGYah3coSG3BZvdiQAAADAkihXFmYYhv64aqcOfXtBzRvalXd/R9lsNrNjAQAAAJZEubKw97Z+o9UlR+XvZ9PchzopvF6g2ZEAAAAAy6JcWVTpyUpNXL1LkjTq7pvVJb6xyYkAAAAAa6NcWVBVjUPDl23XdzUO3Z5wk357Z2uzIwEAAACWR7myoKkf7NZXxyt1U4Mgzc7sKD8/zrMCAAAAvI1yZTF/23FMb286LEmaPbCTmoWGmJwIAAAAqBsoVxZSdvqCfv+XLyVJv72rtXrc3NTkRAAAAEDdQbmyiBqHU0+/s12VVZeUGtdQo+6+2exIAAAAQJ1CubKIF9bvVUnZWYWFBGjOoM4K9OdHCwAAAPyc+ARuAcX7Tmlx8deSpFn3pyi2cT2TEwEAAAB1D+WqljtZUaVRy0skSY/cFqc+HaLMDQQAAADUUZSrWszhNJSzvETfnq9W28hQ/bFvO7MjAQAAAHUW5aoWW/hxqf75r29lD/TX/IdTFRLob3YkAAAAoM6iXNVSWw6e1kt/3y9JmtI/WQnNGpicCAAAAKjbKFe10NkL1Rr5znY5nIYGdIrWA11izI4EAAAA1HmUq1rGMAyN/fOXOlpepRZN6mnarzrIZrOZHQsAAACo8yhXtcwbGw6pcPcJBfn7af7DqWoQHGB2JAAAAACiXNUqO4+Ua/pf90iSxt/TVu2bh5ucCAAAAMBl7PbwYQ6noU0HTmvrv22qt/eUpq/dq2qHUxlJERrarYXZ8QAAAAD8D5QrH7Vu5zFNXrNbx8qrJPnrjf3bJUkN6wXq+QdSOM8KAAAA8DEcFuiD1u08pt++te3/Fyt3Zy/UaNOBb01IBQAAAOD7UK58jMNpaPKa3TKucb9N0uQ1u+VwXmsLAAAAAGagXPmYzQdOX3WP1WWGpGPlVdp84PTPFwoAAADAD6Jc+ZiTldcuVjeyHQAAAICfB+XKxzQLDfHodgAAAAB+HpQrH3Nry8aKCg/Rta4FaJMUFR6iW1s2/jljAQAAAPgBPlGuXn75ZbVo0UIhISHq2rWrNm/efM1tly5dKpvN5raEhFx7L86TTz4pm82m/Px8LyT3PH8/myb2aydJVxSsy7cn9msnfz8uxQ4AAAD4EtPL1fLlyzVq1ChNnDhR27ZtU8eOHdW7d2+dPHnymo8JCwvTsWPHXMuhQ4euut3KlSu1ceNGRUdHeyu+V/yyfZQWPpKqyHD30hgZHqKFj6Tql+2jTEoGAAAA4FpM/yPCs2fP1hNPPKFHH31UkrRo0SL99a9/1euvv65x48Zd9TE2m02RkZHf+7xHjhzR008/rYKCAvXt29fjub3tl+2jdHe7SG0oPan1n25Srzu6Kj2hGXusAAAAAB9larmqrq7W1q1bNX78eNc6Pz8/ZWRkaMOGDdd83Llz5xQfHy+n06nU1FTNmDFDycnJrvudTqeGDBmisWPHuq2/losXL+rixYuu2xUVFZKkmpoa1dTU3Mhb85jUmFB9e5Oh1JhQOR2X5HSYGsdyLv98zf45Wxkz9i7m613M17uYr3cxX+9ivt7lS/P9MRlMLVf//ve/5XA4FBER4bY+IiJCX3311VUfk5iYqNdff10pKSkqLy/XCy+8oG7dumnXrl2KiYmRJM2aNUsBAQEaMWLEdeXIzc3V5MmTr1i/fv161atX70e+K+8oLCw0O4KlMV/vY8bexXy9i/l6F/P1LubrXczXu3xhvhcuXLjubU0/LPDHSk9PV3p6uut2t27dlJSUpMWLF2vq1KnaunWr5syZo23btslmu75D6MaPH69Ro0a5bldUVCg2Nla9evVSWFiYx9/Dj1FTU6PCwkLdfffdCgwMNDWLFTFf72PG3sV8vYv5ehfz9S7m613M17t8ab6Xj2q7HqaWq5tuukn+/v46ceKE2/oTJ0784DlVlwUGBqpz584qLS2VJH366ac6efKk4uLiXNs4HA6NHj1a+fn5Onjw4BXPERwcrODg4Ks+t9k/zMt8KYsVMV/vY8bexXy9i/l6F/P1LubrXczXu3xhvj/m9U29WmBQUJC6dOmioqIi1zqn06mioiK3vVPfx+FwaMeOHYqK+s8V9IYMGaIvv/xSJSUlriU6Olpjx45VQUGBV94HAAAAAJh+WOCoUaOUlZWltLQ03XrrrcrPz9f58+ddVw/89a9/rebNmys3N1eSNGXKFN12221KSEjQ2bNn9fzzz+vQoUN6/PHHJUlNmjRRkyZN3F4jMDBQkZGRSkxM/HnfHAAAAIA6w/RylZmZqVOnTum5557T8ePH1alTJ61bt851kYvDhw/Lz++/O9jOnDmjJ554QsePH1ejRo3UpUsX/fOf/1S7du3MegsAAAAAYH65kqThw4dr+PDhV73v448/drv90ksv6aWXXvpRz3+186wAAAAAwJNMPecKAAAAAKyCcgUAAAAAHkC5AgAAAAAPoFwBAAAAgAdQrgAAAADAAyhXAAAAAOABPnEpdl9jGIYkqaKiwuQkUk1NjS5cuKCKigoFBgaaHcdymK/3MWPvYr7exXy9i/l6F/P1LubrXb4038ud4HJH+D6Uq6uorKyUJMXGxpqcBAAAAIAvqKysVHh4+PduYzOup4LVMU6nU0ePHlVoaKhsNpupWSoqKhQbG6uysjKFhYWZmsWKmK/3MWPvYr7exXy9i/l6F/P1LubrXb40X8MwVFlZqejoaPn5ff9ZVey5ugo/Pz/FxMSYHcNNWFiY6b9YVsZ8vY8Zexfz9S7m613M17uYr3cxX+/ylfn+0B6ry7igBQAAAAB4AOUKAAAAADyAcuXjgoODNXHiRAUHB5sdxZKYr/cxY+9ivt7FfL2L+XoX8/Uu5utdtXW+XNACAAAAADyAPVcAAAAA4AGUKwAAAADwAMoVAAAAAHgA5QoAAAAAPIBy5aNyc3N1yy23KDQ0VM2aNdOAAQO0d+9es2NZxsKFC5WSkuL6w3Tp6elau3at2bEsa+bMmbLZbMrJyTE7iiVMmjRJNpvNbWnbtq3ZsSzlyJEjeuSRR9SkSRPZ7XZ16NBBn3/+udmxLKFFixZX/P7abDZlZ2ebHc0SHA6HJkyYoJYtW8put6t169aaOnWquH6Z51RWVionJ0fx8fGy2+3q1q2btmzZYnasWuuTTz5Rv379FB0dLZvNplWrVrndbxiGnnvuOUVFRclutysjI0P79+83J+x1oFz5qOLiYmVnZ2vjxo0qLCxUTU2NevXqpfPnz5sdzRJiYmI0c+ZMbd26VZ9//rl+8YtfqH///tq1a5fZ0Sxny5YtWrx4sVJSUsyOYinJyck6duyYa/nss8/MjmQZZ86cUffu3RUYGKi1a9dq9+7devHFF9WoUSOzo1nCli1b3H53CwsLJUkPPvigycmsYdasWVq4cKHmz5+vPXv2aNasWcrLy9O8efPMjmYZjz/+uAoLC/Xmm29qx44d6tWrlzIyMnTkyBGzo9VK58+fV8eOHfXyyy9f9f68vDzNnTtXixYt0qZNm1S/fn317t1bVVVVP3PS68Ol2GuJU6dOqVmzZiouLlaPHj3MjmNJjRs31vPPP6/HHnvM7CiWce7cOaWmpmrBggWaNm2aOnXqpPz8fLNj1XqTJk3SqlWrVFJSYnYUSxo3bpz+8Y9/6NNPPzU7Sp2Qk5OjDz74QPv375fNZjM7Tq137733KiIiQq+99ppr3f333y+73a633nrLxGTW8N133yk0NFSrV69W3759Xeu7dOmiPn36aNq0aSamq/1sNptWrlypAQMGSPrPXqvo6GiNHj1aY8aMkSSVl5crIiJCS5cu1aBBg0xMe3XsuaolysvLJf2nAMCzHA6H3n33XZ0/f17p6elmx7GU7Oxs9e3bVxkZGWZHsZz9+/crOjparVq10uDBg3X48GGzI1nG+++/r7S0ND344INq1qyZOnfurFdffdXsWJZUXV2tt956S8OGDaNYeUi3bt1UVFSkffv2SZK++OILffbZZ+rTp4/Jyazh0qVLcjgcCgkJcVtvt9s5gsALDhw4oOPHj7t9jggPD1fXrl21YcMGE5NdW4DZAfDDnE6ncnJy1L17d7Vv397sOJaxY8cOpaenq6qqSg0aNNDKlSvVrl07s2NZxrvvvqtt27ZxHLoXdO3aVUuXLlViYqKOHTumyZMn64477tDOnTsVGhpqdrxa7+uvv9bChQs1atQoPfvss9qyZYtGjBihoKAgZWVlmR3PUlatWqWzZ89q6NChZkexjHHjxqmiokJt27aVv7+/HA6Hpk+frsGDB5sdzRJCQ0OVnp6uqVOnKikpSREREXrnnXe0YcMGJSQkmB3Pco4fPy5JioiIcFsfERHhus/XUK5qgezsbO3cuZNvRDwsMTFRJSUlKi8v15///GdlZWWpuLiYguUBZWVlGjlypAoLC6/4dg8/3f/8BjolJUVdu3ZVfHy8VqxYwWGtHuB0OpWWlqYZM2ZIkjp37qydO3dq0aJFlCsPe+2119SnTx9FR0ebHcUyVqxYobffflvLli1TcnKySkpKlJOTo+joaH5/PeTNN9/UsGHD1Lx5c/n7+ys1NVUPPfSQtm7danY0+AAOC/Rxw4cP1wcffKCPPvpIMTExZsexlKCgICUkJKhLly7Kzc1Vx44dNWfOHLNjWcLWrVt18uRJpaamKiAgQAEBASouLtbcuXMVEBAgh8NhdkRLadiwoW6++WaVlpaaHcUSoqKirviSJSkpiUMvPezQoUP6+9//rscff9zsKJYyduxYjRs3ToMGDVKHDh00ZMgQPfPMM8rNzTU7mmW0bt1axcXFOnfunMrKyrR582bV1NSoVatWZkeznMjISEnSiRMn3NafOHHCdZ+voVz5KMMwNHz4cK1cuVIffvihWrZsaXYky3M6nbp48aLZMSyhZ8+e2rFjh0pKSlxLWlqaBg8erJKSEvn7+5sd0VLOnTunf/3rX4qKijI7iiV07979ij99sW/fPsXHx5uUyJqWLFmiZs2auV0UAD/dhQsX5Ofn/vHO399fTqfTpETWVb9+fUVFRenMmTMqKChQ//79zY5kOS1btlRkZKSKiopc6yoqKrRp0yafPU+ewwJ9VHZ2tpYtW6bVq1crNDTUdVxpeHi47Ha7yelqv/Hjx6tPnz6Ki4tTZWWlli1bpo8//lgFBQVmR7OE0NDQK84PrF+/vpo0acJ5gx4wZswY9evXT/Hx8Tp69KgmTpwof39/PfTQQ2ZHs4RnnnlG3bp104wZMzRw4EBt3rxZr7zyil555RWzo1mG0+nUkiVLlJWVpYAAPop4Ur9+/TR9+nTFxcUpOTlZ27dv1+zZszVs2DCzo1lGQUGBDMNQYmKiSktLNXbsWLVt21aPPvqo2dFqpXPnzrkdeXHgwAGVlJSocePGiouLU05OjqZNm6Y2bdqoZcuWmjBhgqKjo11XFPQ5BnySpKsuS5YsMTuaJQwbNsyIj483goKCjKZNmxo9e/Y01q9fb3YsS7vzzjuNkSNHmh3DEjIzM42oqCgjKCjIaN68uZGZmWmUlpaaHctS1qxZY7Rv394IDg422rZta7zyyitmR7KUgoICQ5Kxd+9es6NYTkVFhTFy5EgjLi7OCAkJMVq1amX84Q9/MC5evGh2NMtYvny50apVKyMoKMiIjIw0srOzjbNnz5odq9b66KOPrvqZNysryzAMw3A6ncaECROMiIgIIzg42OjZs6dP/9vB37kCAAAAAA/gnCsAAAAA8ADKFQAAAAB4AOUKAAAAADyAcgUAAAAAHkC5AgAAAAAPoFwBAAAAgAdQrgAAAADAAyhXAAAAAOABlCsAAEwwdOhQDRgwwOwYAAAPolwBAEw1dOhQ2Ww2zZw50239qlWrZLPZTMm0dOlS2Wy2710OHjz4k15jzpw5Wrp0qUfyAgB8A+UKAGC6kJAQzZo1S2fOnDE7iiQpMzNTx44dcy3p6el64okn3NbFxsb+pNcIDw9Xw4YNPRMYAOATKFcAANNlZGQoMjJ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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(LDA_coherence_score_df['Num_Topics'], LDA_coherence_score_df['Coherence_Score'], marker='o')\n",
    "\n",
    "plt.xlabel('Num_Topics')\n",
    "plt.ylabel('Coherence_Score')\n",
    "plt.title('LDA: Coherence Score vs Number of Topics')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deGRIU-yc_Op",
   "metadata": {
    "id": "deGRIU-yc_Op"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3HqQZy43QlCd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3HqQZy43QlCd",
    "outputId": "03225759-c454-4c1d-b480-abeb7e7a959c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating Perplexity_Score:   0%|          | 0/5 [00:00<?, ?it/s]<ipython-input-18-7cddf270ee62>:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  perplexity_results_df = perplexity_results_df.append({'Num_Topics': num_topics, 'Perplexity_Score': perplexity}, ignore_index=True)\n",
      "Calculating Perplexity_Score:  20%|██        | 1/5 [12:07<48:29, 727.32s/it]<ipython-input-18-7cddf270ee62>:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  perplexity_results_df = perplexity_results_df.append({'Num_Topics': num_topics, 'Perplexity_Score': perplexity}, ignore_index=True)\n",
      "Calculating Perplexity_Score:  40%|████      | 2/5 [27:14<41:38, 832.85s/it]<ipython-input-18-7cddf270ee62>:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  perplexity_results_df = perplexity_results_df.append({'Num_Topics': num_topics, 'Perplexity_Score': perplexity}, ignore_index=True)\n",
      "Calculating Perplexity_Score:  60%|██████    | 3/5 [42:21<28:53, 866.83s/it]<ipython-input-18-7cddf270ee62>:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  perplexity_results_df = perplexity_results_df.append({'Num_Topics': num_topics, 'Perplexity_Score': perplexity}, ignore_index=True)\n",
      "Calculating Perplexity_Score:  80%|████████  | 4/5 [58:02<14:56, 896.25s/it]<ipython-input-18-7cddf270ee62>:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  perplexity_results_df = perplexity_results_df.append({'Num_Topics': num_topics, 'Perplexity_Score': perplexity}, ignore_index=True)\n",
      "Calculating Perplexity_Score: 100%|██████████| 5/5 [1:13:52<00:00, 886.46s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "# Range to find the best number of topics\n",
    "start = 2\n",
    "stop = 12\n",
    "step = 2\n",
    "\n",
    "# Calculations to find best number of topics\n",
    "def calculate_perplexity(lda_model, doc_term_matrix):\n",
    "     return lda_model.log_perplexity(doc_term_matrix)\n",
    "\n",
    "perplexity_results_df = pd.DataFrame(columns=['Num_Topics', 'Perplexity_Score'])\n",
    "\n",
    "# Apply to LDA model\n",
    "for num_topics in tqdm(range(start, stop, step), desc=\"Calculating Perplexity_Score\"):\n",
    "     lda_model = models.LdaModel(corpus=doc_term_matrix, id2word=dictionary, num_topics=num_topics, random_state=42)\n",
    "     perplexity = calculate_perplexity(lda_model, doc_term_matrix)\n",
    "     perplexity_results_df = perplexity_results_df.append({'Num_Topics': num_topics, 'Perplexity_Score': perplexity}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "Jsg_jDL8-yGy",
   "metadata": {
    "id": "Jsg_jDL8-yGy"
   },
   "outputs": [],
   "source": [
    "perplexity_results_df.to_csv('/content/drive/MyDrive/M2/Perplexity_Score_by_Topic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "lsUqW0W_-4He",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "id": "lsUqW0W_-4He",
    "outputId": "f20ce67e-cde1-4474-dbe1-9e27490fa397"
   },
   "outputs": [
    {
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       "          const element = document.querySelector('#df-f8991b35-4f9d-44ec-878f-cdcdbe6c742c');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   Num_Topics  Perplexity_Score\n",
       "0         2.0         -7.398622\n",
       "1         4.0         -7.409868\n",
       "2         6.0         -7.513597\n",
       "3         8.0         -7.636319\n",
       "4        10.0         -7.881360"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perplexity_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3TXUWaXC_JZ9",
   "metadata": {
    "id": "3TXUWaXC_JZ9"
   },
   "outputs": [],
   "source": [
    "path=\"/content/drive/MyDrive/M2/Perplexity_Score_by_Topic.csv\"\n",
    "\n",
    "\n",
    "LDA_perplexity_score_df = pd.read_csv(path, lineterminator = '\\n')"
   ]
  },
  {
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   "id": "XBBzffe9_QvO",
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    },
    "id": "XBBzffe9_QvO",
    "outputId": "a2d2d1db-8b10-4e78-a726-ccb8356be427"
   },
   "outputs": [
    {
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       "   Unnamed: 0  Num_Topics  Perplexity_Score\n",
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    "id": "MYXDXVmTfkQs",
    "outputId": "57414d85-ca6f-475e-e128-bffe85634016"
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    {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(LDA_perplexity_score_df['Num_Topics'], LDA_perplexity_score_df['Perplexity_Score'], marker='o')\n",
    "\n",
    "plt.xlabel('Num_Topics')\n",
    "plt.ylabel('Perplexity_Score')\n",
    "plt.title('LDA: Perplexity Score vs Number of Topics')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "_Pt8UP0jtOBc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "id": "_Pt8UP0jtOBc",
    "outputId": "48dd6e68-9a30-43ad-9548-a9c1a1501554"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LatentDirichletAllocation(learning_method='online', n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 20 to 21 min\n",
    "#%%time\n",
    "\n",
    "RANDOM_SEED = 42\n",
    "number_topics = 6 ### based on coherence score\n",
    "number_words = 10\n",
    "\n",
    "bag_of_words = count_vectorizer.transform(LDA_df['cleaned_comments_joined'])\n",
    "\n",
    "# Create and fit the LDA model\n",
    "lda = LDA(n_components=number_topics, random_state=RANDOM_SEED, n_jobs=-1, learning_method='online')\n",
    "lda.fit(bag_of_words)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "t0nVy83r3Szt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t0nVy83r3Szt",
    "outputId": "ed0bacdb-d0d5-4110-9e20-c6dfb1631ca3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic #1:\n",
      "easy parking location clean close check comfortable area host safe\n",
      "\n",
      "Topic #2:\n",
      "host best experience location back always feel made clean recommend\n",
      "\n",
      "Topic #3:\n",
      "host location clean responsive wonderful comfortable location clean accommodating clean comfortable host responsive\n",
      "\n",
      "Topic #4:\n",
      "neighborhood awesome quiet convenient helpful clean back spacious time quiet neighborhood\n",
      "\n",
      "Topic #5:\n",
      "host exactly recommend clean time needed beautiful comfortable staying picture\n",
      "\n",
      "Topic #6:\n",
      "beautiful view space location host getaway little comfy outdoor peaceful forward\n",
      "\n",
      "Topic #7:\n",
      "bed value bathroom overall parking issue kitchen little thing clean\n",
      "\n",
      "Topic #8:\n",
      "recommend anyone staying recommend anyone thanks looking hospitality location people host\n",
      "\n",
      "Topic #9:\n",
      "clean quick communication excellent easy price enjoyed location recommended cute\n",
      "\n",
      "Topic #10:\n",
      "beach restaurant distance walking walking distance close walk shop location short\n"
     ]
    }
   ],
   "source": [
    "print_topics(lda, count_vectorizer, number_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cmcU2OWjc1l3",
   "metadata": {
    "id": "cmcU2OWjc1l3"
   },
   "source": [
    "## 3. Topic exploration using pyLDAvis\n",
    "Leveraging the instruction provided in the SIADS 543 Unsupervised Learning course, we employed pyLDAvis for our topic modelling exploration. The coursework material and the resource at https://github.com/bmabey/pyLDAvis served as guides for our implementation process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfc54dbe-8b40-492b-975a-5ca5b533c1dc",
   "metadata": {
    "id": "cfc54dbe-8b40-492b-975a-5ca5b533c1dc"
   },
   "outputs": [],
   "source": [
    "def lda_gen(clean_data):\n",
    "\n",
    "  # Making dictionary\n",
    "    dictionary = corpora.Dictionary(clean_data)\n",
    "  # Remove extremes\n",
    "    dictionary.filter_extremes(no_below=2, no_above=0.5)\n",
    "  # Making corpus\n",
    "    corpus = [dictionary.doc2bow(text) for text in clean_data]\n",
    "  # Making model\n",
    "    num_topics = 6 #### since coherene score shows best number of topics is 6\n",
    "    chunksize = 2000\n",
    "    passes = 20\n",
    "    iterations = 400\n",
    "    eval_every = None\n",
    "\n",
    "    temp = dictionary[0]\n",
    "    id2word = dictionary.id2token\n",
    "\n",
    "    model = LdaModel(\n",
    "      corpus=corpus,\n",
    "      id2word=id2word,\n",
    "      chunksize=chunksize,\n",
    "      alpha='auto',\n",
    "      eta='auto',\n",
    "      iterations=iterations,\n",
    "      num_topics=num_topics,\n",
    "      passes=passes,\n",
    "      eval_every=eval_every\n",
    "  )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cef2232-0555-4a5b-a047-46536dacd34c",
   "metadata": {
    "id": "7cef2232-0555-4a5b-a047-46536dacd34c"
   },
   "outputs": [],
   "source": [
    "def print_topics(sentiment):\n",
    "\n",
    "    if sentiment == 'pos':\n",
    "\n",
    "        dictionary = corpora.Dictionary(positive_df['cleaned_comments_nltk'])\n",
    "        corpus = [dictionary.doc2bow(text) for text in positive_df['cleaned_comments_nltk']]\n",
    "        top_topics = lda_gen(positive_df['cleaned_comments_nltk']).top_topics(corpus)\n",
    "    elif sentiment == 'neg':\n",
    "        dictionary = corpora.Dictionary(negative_df['cleaned_comments_nltk'])\n",
    "        corpus = [dictionary.doc2bow(text) for text in negative_df['cleaned_comments_nltk']]\n",
    "        top_topics = lda_gen(negative_df['cleaned_comments_nltk']).top_topics(corpus)\n",
    "    elif sentiment == 'neu':\n",
    "        dictionary = corpora.Dictionary(neutral_df['cleaned_comments_nltk'])\n",
    "        corpus = [dictionary.doc2bow(text) for text in neutral_df['cleaned_comments_nltk']]\n",
    "        top_topics = lda_gen(neutral_df['cleaned_comments_nltk']).top_topics(corpus)\n",
    "\n",
    "    print(top_topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d408fc45-e356-45d1-a2a7-00d8235f80e3",
   "metadata": {
    "id": "d408fc45-e356-45d1-a2a7-00d8235f80e3"
   },
   "outputs": [],
   "source": [
    "#Positive sentiment dataframe\n",
    "positive_df = LDA_df[LDA_df['Sentiment']=='Positive']\n",
    "#Negative sentiment dataframe\n",
    "negative_df = LDA_df[LDA_df['Sentiment']=='Negative']\n",
    "#Neutral sentiment dataframe\n",
    "neutral_df = LDA_df[LDA_df['Sentiment']=='Neutral']"
   ]
  },
  {
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    "outputId": "6f1a8806-bb8f-496f-db2d-268fe87b5477"
   },
   "outputs": [
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       "            + ' to learn more about interactive tables.';\n",
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      "text/plain": [
       "    Unnamed: 0  listing_id reviewer_name  \\\n",
       "0            0         109         Edwin   \n",
       "27          27        2708          Haji   \n",
       "42          42      180926          Nick   \n",
       "43          43      180926          Nora   \n",
       "87          87      180926         Rocco   \n",
       "\n",
       "                                            comments2      id  \\\n",
       "0   The host canceled my reservation the day befor...     109   \n",
       "27  Top notch spot to stay within walking distance...    2708   \n",
       "42  The host canceled this reservation 2 days befo...  180926   \n",
       "43  The host canceled this reservation 11 days bef...  180926   \n",
       "87  Absolute gem if youre looking for a nononsense...  180926   \n",
       "\n",
       "    final_review_score score_label  Sentiment_Score Sentiment  \\\n",
       "0             4.285714        high           0.0000   Neutral   \n",
       "27            4.912857        high           0.2023   Neutral   \n",
       "42            4.905714        high           0.0000   Neutral   \n",
       "43            4.905714        high           0.0000   Neutral   \n",
       "87            4.905714        high           0.3182   Neutral   \n",
       "\n",
       "    normalized_Sentiment_score  normalize_rating_score  \\\n",
       "0                         2.50                    4.25   \n",
       "27                        3.00                    5.00   \n",
       "42                        2.50                    5.00   \n",
       "43                        2.50                    5.00   \n",
       "87                        3.25                    5.00   \n",
       "\n",
       "                                cleaned_comments_nltk  \\\n",
       "0              [host, canceled, reservation, arrival]   \n",
       "27  [top, notch, spot, within, walking, distance, ...   \n",
       "42  [host, canceled, reservation, arrival, automat...   \n",
       "43  [host, canceled, reservation, arrival, automat...   \n",
       "87  [absolute, gem, youre, looking, nononsense, ou...   \n",
       "\n",
       "                              cleaned_comments_joined  \n",
       "0                   host canceled reservation arrival  \n",
       "27  top notch spot within walking distance west ho...  \n",
       "42  host canceled reservation arrival automated po...  \n",
       "43  host canceled reservation arrival automated po...  \n",
       "87  absolute gem youre looking nononsense outdoora...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neutral_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aca9240-5f3d-4623-940b-1a27b9ff0284",
   "metadata": {
    "id": "3aca9240-5f3d-4623-940b-1a27b9ff0284",
    "outputId": "78ca1d5e-8e61-48e0-a101-a8474d2b6857"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing positive topcis\n",
      "==========================\n",
      "[([(0.034357402, 'easy'), (0.028817689, 'space'), (0.028116643, 'parking'), (0.024793152, 'bed'), (0.023322953, 'also'), (0.019288728, 'check'), (0.01799362, 'kitchen'), (0.01765134, 'lot'), (0.01498984, 'little'), (0.014252268, 'bathroom'), (0.013014687, 'street'), (0.012833639, 'area'), (0.011315319, 'right'), (0.011242067, 'private'), (0.010229874, 'around'), (0.009882366, 'bedroom'), (0.0087569095, 'overall'), (0.00863347, 'work'), (0.008376087, 'two'), (0.008302437, 'need')], -2.7270018574654697), ([(0.042139344, 'time'), (0.019630617, 'made'), (0.018734759, 'stayed'), (0.018116603, 'experience'), (0.017951638, 'could'), (0.017924473, 'much'), (0.01787775, 'best'), (0.015979767, 'feel'), (0.015448241, 'next'), (0.014974213, 'make'), (0.014031289, 'back'), (0.013979295, 'sure'), (0.013270946, 'always'), (0.013114859, 'trip'), (0.011843048, 'guest'), (0.011287585, 'go'), (0.01060252, 'every'), (0.010022105, 'better'), (0.009815623, 'want'), (0.009693038, 'first')], -3.0703946958622073), ([(0.07369937, 'close'), (0.050474633, 'beach'), (0.049086995, 'restaurant'), (0.03489604, 'away'), (0.033257175, 'walk'), (0.03120355, 'minute'), (0.030016135, 'walking'), (0.02828967, 'located'), (0.026776392, 'distance'), (0.025205003, 'many'), (0.020815251, 'hollywood'), (0.019034494, 'long'), (0.018862903, 'short'), (0.01851636, 'within'), (0.018480457, 'near'), (0.01817464, 'drive'), (0.017405495, 'store'), (0.017193357, 'downtown'), (0.016596437, 'studio'), (0.016583491, 'shop')], -3.2673357535169236), ([(0.077414736, 'clean'), (0.07422969, 'host'), (0.06677573, 'location'), (0.039428998, 'recommend'), (0.03906057, 'comfortable'), (0.027977565, 'responsive'), (0.024984265, 'quiet'), (0.020838233, 'neighborhood'), (0.01990189, 'needed'), (0.018710155, 'enjoyed'), (0.018336335, 'family'), (0.016888902, 'communication'), (0.016771404, 'helpful'), (0.016266853, 'area'), (0.01619302, 'spacious'), (0.015957493, 'wonderful'), (0.015949583, 'staying'), (0.015174018, 'safe'), (0.01473723, 'back'), (0.014050577, 'need')], -3.345714232206325), ([(0.10930765, 'beautiful'), (0.058432657, 'view'), (0.034376465, 'property'), (0.029281482, 'pool'), (0.026749367, 'peaceful'), (0.01890071, 'backyard'), (0.016117057, 'city'), (0.0153855095, 'enjoy'), (0.015301274, 'cant'), (0.013168079, 'outdoor'), (0.012978374, 'patio'), (0.012856067, 'relaxing'), (0.012002026, 'fun'), (0.011272476, 'gorgeous'), (0.011113539, 'incredible'), (0.0110378945, 'top'), (0.010883883, 'truly'), (0.010671627, 'hill'), (0.010161424, 'absolutely'), (0.009993619, 'morning')], -4.901489080626993), ([(0.02597777, 'issue'), (0.01695157, 'bit'), (0.01614153, 'door'), (0.014628648, 'thing'), (0.014186888, 'building'), (0.013317186, 'dont'), (0.01160486, 'overall'), (0.010815654, 'wasnt'), (0.010620859, 'problem'), (0.010373817, 'unit'), (0.010120032, 'floor'), (0.00968397, 'since'), (0.009567696, 'though'), (0.00956131, 'sleep'), (0.008827474, 'however'), (0.0087792445, 'still'), (0.008747873, 'noise'), (0.008598703, 'window'), (0.008166193, 'open'), (0.008092617, 'neighbor')], -6.31761611226329), ([(0.033410404, 'early'), (0.01889393, 'team'), (0.013887897, 'flight'), (0.012089259, 'roomy'), (0.009721073, 'bowl'), (0.00967667, 'stop'), (0.009615968, 'later'), (0.009533484, 'bus'), (0.009207267, 'pet'), (0.008955788, 'station'), (0.00883561, 'public'), (0.008788555, 'chris'), (0.008648044, 'entry'), (0.008334966, 'burbank'), (0.008236166, 'bag'), (0.008108142, 'seems'), (0.008089862, 'let'), (0.007839005, 'trash'), (0.0078047537, 'locked'), (0.00766676, 'rose')], -7.301410707106176), ([(0.048426855, 'shower'), (0.045886412, 'hot'), (0.04435768, 'towel'), (0.042824194, 'water'), (0.032823414, 'air'), (0.027984563, 'tub'), (0.026557414, 'coffee'), (0.02518488, 'ac'), (0.01980994, 'dryer'), (0.019402692, 'cold'), (0.016642826, 'fridge'), (0.016557284, 'washer'), (0.013977909, 'etc'), (0.013136992, 'fresh'), (0.012169417, 'linen'), (0.011983174, 'heat'), (0.010753134, 'toilet'), (0.010534844, 'paper'), (0.009913248, 'machine'), (0.009548356, 'utensil')], -9.033518662576416)]\n",
      "Printing negative topcis\n",
      "==========================\n",
      "[([(0.023073303, 'bed'), (0.021245489, 'dirty'), (0.0179638, 'bathroom'), (0.014038508, 'clean'), (0.013518482, 'towel'), (0.011695541, 'kitchen'), (0.011532004, 'floor'), (0.009616441, 'broken'), (0.009232524, 'shower'), (0.008674953, 'smell'), (0.007864155, 'bedroom'), (0.0072853738, 'old'), (0.006515521, 'picture'), (0.006261891, 'toilet'), (0.006227775, 'door'), (0.006208162, 'sheet'), (0.0060057505, 'also'), (0.005634321, 'hair'), (0.0056030885, 'cleaning'), (0.0054956595, 'location')], -6.836559227394359), ([(0.009925643, 'also'), (0.009735027, 'door'), (0.008280533, 'dog'), (0.007856519, 'owner'), (0.007765658, 'two'), (0.0071107442, 'dont'), (0.007017763, 'could'), (0.0064129834, 'unit'), (0.00637424, 'window'), (0.0062977765, 'time'), (0.006256684, 'issue'), (0.0062042973, 'every'), (0.0059990487, 'use'), (0.0057518356, 'problem'), (0.0057438444, 'ant'), (0.005556917, 'open'), (0.0054594963, 'see'), (0.0048101204, 'guest'), (0.0047327727, 'around'), (0.0046484196, 'people')], -7.015275228906702), ([(0.03754618, 'parking'), (0.018002357, 'location'), (0.017352724, 'car'), (0.0160684, 'street'), (0.01261003, 'people'), (0.011979281, 'noise'), (0.011691808, 'building'), (0.010209083, 'area'), (0.010162386, 'loud'), (0.010039574, 'also'), (0.010034097, 'neighbor'), (0.009719217, 'lot'), (0.009550352, 'park'), (0.008542155, 'door'), (0.0080685215, 'space'), (0.007979333, 'unit'), (0.007951345, 'garage'), (0.00716445, 'neighborhood'), (0.006901802, 'difficult'), (0.006802338, 'bit')], -7.363012875369189), ([(0.037358195, 'host'), (0.013546971, 'time'), (0.012467808, 'check'), (0.0113077015, 'experience'), (0.010311519, 'never'), (0.009877191, 'said'), (0.009468597, 'told'), (0.008461279, 'back'), (0.008370923, 'hour'), (0.008127044, 'refund'), (0.0077915126, 'got'), (0.0076710046, 'asked'), (0.0072926013, 'guest'), (0.0070041404, 'money'), (0.0065152245, 'dont'), (0.006141477, 'left'), (0.0056901556, 'hotel'), (0.005438501, 'could'), (0.005204162, 'another'), (0.0051898435, 'first')], -7.940212323609123), ([(0.046420094, 'unit'), (0.022568887, 'pool'), (0.0147793, 'address'), (0.012875202, 'view'), (0.01222291, 'jacuzzi'), (0.009388723, 'property'), (0.009239832, 'actual'), (0.008290922, 'provided'), (0.007755574, 'wifi'), (0.007511502, 'password'), (0.00679637, 'instruction'), (0.0067788796, 'listing'), (0.006499927, 'dog'), (0.0064822426, 'cheap'), (0.006406646, 'boat'), (0.006347296, 'hostess'), (0.0062263897, 'different'), (0.006199694, 'west'), (0.005938643, 'totally'), (0.0058676368, 'bare')], -10.217815117734064), ([(0.03723099, 'water'), (0.024020333, 'hot'), (0.020557607, 'issue'), (0.018738685, 'shower'), (0.018490255, 'work'), (0.014184567, 'cold'), (0.012882364, 'heat'), (0.012825754, 'heater'), (0.012333737, 'ac'), (0.01179941, 'working'), (0.011708989, 'unit'), (0.008289924, 'fixed'), (0.008271711, 'alarm'), (0.007990795, 'fix'), (0.007907784, 'door'), (0.0078186095, 'turn'), (0.007632654, 'light'), (0.0072433734, 'problem'), (0.007037269, 'use'), (0.0062360154, 'broken')], -11.993329231195682), ([(0.021333672, 'gas'), (0.010047007, 'live'), (0.009151229, 'due'), (0.008987899, 'experience'), (0.008138087, 'setting'), (0.008092691, 'poor'), (0.0077748434, 'small'), (0.007379964, 'pic'), (0.006860002, 'cold'), (0.0064760647, 'sick'), (0.0061737457, 'price'), (0.006124906, 'lack'), (0.005914383, 'bit'), (0.005832625, 'clean'), (0.005725126, 'without'), (0.005711728, 'deadbolt'), (0.0055521745, 'rv'), (0.0055235308, 'able'), (0.0053639654, 'within'), (0.005261937, 'since')], -12.303364497078636), ([(0.035574533, 'property'), (0.0135544045, 'owner'), (0.0074782106, 'building'), (0.0067951255, 'sent'), (0.0065440796, 'manager'), (0.0060873344, 'company'), (0.0060842405, 'photo'), (0.0060167755, 'move'), (0.005883121, 'damage'), (0.005821747, 'care'), (0.005085782, 'rental'), (0.0048920666, 'booked'), (0.0048577045, 'family'), (0.004794278, 'management'), (0.004529883, 'claim'), (0.004519786, 'lied'), (0.00451089, 'allowed'), (0.0044726455, 'human'), (0.004330086, 'condition'), (0.004163903, 'east')], -12.581179547007498)]\n",
      "Printing neutral topcis\n",
      "==========================\n",
      "[([(0.023066767, 'also'), (0.021714337, 'unit'), (0.019831818, 'door'), (0.01618191, 'people'), (0.0152383745, 'overall'), (0.013149002, 'two'), (0.012263762, 'bit'), (0.012200671, 'guest'), (0.010251274, 'made'), (0.0101945065, 'outside'), (0.010010438, 'feel'), (0.009944244, 'thing'), (0.009927647, 'noise'), (0.009900851, 'however'), (0.0089543965, 'sleep'), (0.008915628, 'around'), (0.008472047, 'building'), (0.008191, 'wall'), (0.007857364, 'someone'), (0.007832982, 'neighbor')], -6.4185546602812265), ([(0.034328055, 'check'), (0.021719119, 'never'), (0.019994745, 'got'), (0.016521337, 'make'), (0.015661744, 'said'), (0.015425221, 'listing'), (0.013585144, 'hour'), (0.013174342, 'know'), (0.012986849, 'left'), (0.012472075, 'another'), (0.011987729, 'sure'), (0.011614616, 'owner'), (0.010874171, 'arrived'), (0.010743757, 'let'), (0.010224556, 'thanks'), (0.010035621, 'instruction'), (0.009655706, 'still'), (0.009622403, 'booked'), (0.009307252, 'told'), (0.0090406425, 'review')], -7.293073881293942), ([(0.034628693, 'bed'), (0.02708123, 'bathroom'), (0.019823318, 'kitchen'), (0.019698914, 'work'), (0.016766062, 'shower'), (0.016465351, 'floor'), (0.015829172, 'water'), (0.014985385, 'towel'), (0.014687831, 'use'), (0.014417301, 'dirty'), (0.012757699, 'bedroom'), (0.011437609, 'hot'), (0.009944571, 'tv'), (0.009141263, 'wasnt'), (0.008964672, 'cold'), (0.0088653425, 'pool'), (0.008582638, 'comfortable'), (0.008576888, 'working'), (0.00854906, 'toilet'), (0.0084712645, 'fine')], -9.846933860032413), ([(0.09877321, 'host'), (0.042708926, 'time'), (0.034347463, 'back'), (0.030452358, 'issue'), (0.019101901, 'book'), (0.018463496, 'quick'), (0.01834483, 'accommodating'), (0.017997941, 'responsive'), (0.017258298, 'staying'), (0.016906733, 'stayed'), (0.016534694, 'communication'), (0.015451307, 'problem'), (0.0153731285, 'go'), (0.014774412, 'family'), (0.01364152, 'next'), (0.012392501, 'always'), (0.012159846, 'trip'), (0.011993553, 'response'), (0.011364135, 'refund'), (0.010476642, 'minute')], -9.958561894513544), ([(0.035061937, 'recommend'), (0.023592098, 'price'), (0.021985894, 'experience'), (0.019605782, 'dont'), (0.019331673, 'picture'), (0.01870579, 'much'), (0.017914126, 'exactly'), (0.016953763, 'described'), (0.016266577, 'say'), (0.015090746, 'view'), (0.014826362, 'enough'), (0.014554646, 'every'), (0.014531365, 'youre'), (0.014477726, 'advertised'), (0.013844541, 'expected'), (0.013492749, 'comfy'), (0.012909452, 'look'), (0.012684006, 'better'), (0.012466922, 'couldnt'), (0.012148522, 'want')], -11.450166612618585), ([(0.09163809, 'location'), (0.08269146, 'clean'), (0.038873207, 'close'), (0.036314256, 'quiet'), (0.03601265, 'area'), (0.035388, 'need'), (0.028986644, 'convenient'), (0.028939275, 'space'), (0.025856962, 'cozy'), (0.023310235, 'little'), (0.02182215, 'needed'), (0.021241859, 'beach'), (0.02086133, 'spacious'), (0.020586606, 'spot'), (0.020344242, 'neighborhood'), (0.019439664, 'lot'), (0.01734633, 'away'), (0.014682807, 'located'), (0.0141962655, 'restaurant'), (0.012955167, 'walk')], -11.548760740248973), ([(0.1052988, 'parking'), (0.043932438, 'street'), (0.032070708, 'easy'), (0.030041747, 'car'), (0.025815444, 'walking'), (0.024117868, 'find'), (0.021748638, 'big'), (0.020780846, 'garage'), (0.01943217, 'access'), (0.018546754, 'distance'), (0.018008873, 'checkin'), (0.01744926, 'address'), (0.016468087, 'park'), (0.015088477, 'difficult'), (0.010792377, 'within'), (0.010758584, 'food'), (0.010620836, 'plus'), (0.009832302, 'central'), (0.009767363, 'grocery'), (0.00938777, 'modern')], -13.63863452915307), ([(0.10458642, 'arrival'), (0.10158222, 'reservation'), (0.09057739, 'host'), (0.065926306, 'canceled'), (0.059350487, 'posting'), (0.057677563, 'automated'), (0.043171875, 'cup'), (0.020804849, 'likely'), (0.014837751, 'yellow'), (0.014519636, 'lied'), (0.013892962, 'funny'), (0.013498709, 'plain'), (0.0131688975, 'receive'), (0.011929342, 'yun'), (0.01181287, 'tended'), (0.011605644, 'plumbing'), (0.009151808, 'cloth'), (0.009034347, 'specifically'), (0.008477347, 'happening'), (0.00844626, 'tea')], -15.904091221431647)]\n"
     ]
    }
   ],
   "source": [
    "print(\"Printing positive topcis\")\n",
    "print(\"==========================\")\n",
    "print_topics('pos')\n",
    "print(\"Printing negative topcis\")\n",
    "print(\"==========================\")\n",
    "print_topics('neg')\n",
    "print(\"Printing neutral topcis\")\n",
    "print(\"==========================\")\n",
    "print_topics('neu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a9adf45-0947-4973-8612-cc364d75d6b7",
   "metadata": {
    "id": "3a9adf45-0947-4973-8612-cc364d75d6b7"
   },
   "outputs": [],
   "source": [
    "def lda_vis(model,clean_data):\n",
    "\n",
    "    !pip install pyLDAvis\n",
    "    import pickle\n",
    "    import pyLDAvis.gensim_models as gensimvis\n",
    "    import pyLDAvis\n",
    "    if clean_data =='pos':\n",
    "        clean_data = positive_df['cleaned_comments_nltk']\n",
    "        dictionary = corpora.Dictionary(clean_data)\n",
    "    # Remove extremes\n",
    "        dictionary.filter_extremes(no_below=2, no_above=0.5)\n",
    "    # Making corpus\n",
    "        corpus = [dictionary.doc2bow(text) for text in clean_data]\n",
    "        lda_visualization = gensimvis.prepare(model,corpus,dictionary, sort_topics=False)\n",
    "        pyLDAvis.save_html(lda_visualization, 'LDA visualizations_positive.html')\n",
    "    elif clean_data =='neg':\n",
    "        clean_data = negative_df['cleaned_comments_nltk']\n",
    "        dictionary = corpora.Dictionary(clean_data)\n",
    "        # Remove extremes\n",
    "        dictionary.filter_extremes(no_below=2, no_above=0.5)\n",
    "    # Making corpus\n",
    "        corpus = [dictionary.doc2bow(text) for text in clean_data]\n",
    "        lda_visualization = gensimvis.prepare(model,corpus,dictionary, sort_topics=False)\n",
    "        pyLDAvis.save_html(lda_visualization, 'LDA visualizations_negative.html')\n",
    "    elif clean_data =='neu':\n",
    "        clean_data = neutral_df['cleaned_comments_nltk']\n",
    "        dictionary = corpora.Dictionary(clean_data)\n",
    "    # Remove extremes\n",
    "        dictionary.filter_extremes(no_below=2, no_above=0.5)\n",
    "    # Making corpus\n",
    "        corpus = [dictionary.doc2bow(text) for text in clean_data]\n",
    "        lda_visualization = gensimvis.prepare(model,corpus,dictionary, sort_topics=False)\n",
    "        pyLDAvis.save_html(lda_visualization, 'LDA visualizations_neutral.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06e08dbf-699d-4438-a29c-3b22bd55c3cb",
   "metadata": {
    "id": "06e08dbf-699d-4438-a29c-3b22bd55c3cb",
    "outputId": "831a6f07-8470-4e53-bf75-a3e1af91c654"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pyLDAvis\n",
      "  Downloading pyLDAvis-3.4.1-py3-none-any.whl (2.6 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m0m\n",
      "\u001b[?25hCollecting numpy>=1.24.2 (from pyLDAvis)\n",
      "  Downloading numpy-1.24.3-cp39-cp39-macosx_10_9_x86_64.whl (19.8 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.8/19.8 MB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0mm\n",
      "\u001b[?25hRequirement already satisfied: scipy in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (1.7.1)\n",
      "Requirement already satisfied: pandas>=2.0.0 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (2.0.1)\n",
      "Collecting joblib>=1.2.0 (from pyLDAvis)\n",
      "  Downloading joblib-1.2.0-py3-none-any.whl (297 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m298.0/298.0 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: jinja2 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (2.11.3)\n",
      "Requirement already satisfied: numexpr in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (2.7.3)\n",
      "Collecting funcy (from pyLDAvis)\n",
      "  Downloading funcy-2.0-py2.py3-none-any.whl (30 kB)\n",
      "Requirement already satisfied: scikit-learn>=1.0.0 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (1.1.1)\n",
      "Requirement already satisfied: gensim in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (4.2.0)\n",
      "Requirement already satisfied: setuptools in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (58.0.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pandas>=2.0.0->pyLDAvis) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pandas>=2.0.0->pyLDAvis) (2022.7.1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pandas>=2.0.0->pyLDAvis) (2022.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=1.0.0->pyLDAvis) (2.2.0)\n",
      "INFO: pip is looking at multiple versions of scipy to determine which version is compatible with other requirements. This could take a while.\n",
      "Collecting scipy (from pyLDAvis)\n",
      "  Downloading scipy-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl (35.2 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.2/35.2 MB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0mm\n",
      "\u001b[?25hRequirement already satisfied: smart-open>=1.8.1 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from gensim->pyLDAvis) (5.2.1)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from jinja2->pyLDAvis) (1.1.1)\n",
      "Requirement already satisfied: six>=1.5 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas>=2.0.0->pyLDAvis) (1.16.0)\n",
      "Installing collected packages: funcy, numpy, joblib, scipy, pyLDAvis\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.22.4\n",
      "    Uninstalling numpy-1.22.4:\n",
      "      Successfully uninstalled numpy-1.22.4\n",
      "  Attempting uninstall: joblib\n",
      "    Found existing installation: joblib 1.1.1\n",
      "    Uninstalling joblib-1.1.1:\n",
      "      Successfully uninstalled joblib-1.1.1\n",
      "  Attempting uninstall: scipy\n",
      "    Found existing installation: scipy 1.7.1\n",
      "    Uninstalling scipy-1.7.1:\n",
      "      Successfully uninstalled scipy-1.7.1\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "daal4py 2021.3.0 requires daal==2021.2.3, which is not installed.\n",
      "dtale 2.15.2 requires matplotlib<=3.6.0; python_version == \"3.9\", but you have matplotlib 3.7.1 which is incompatible.\n",
      "dtale 2.15.2 requires scipy<=1.9.3; python_version >= \"3.8\", but you have scipy 1.10.1 which is incompatible.\n",
      "numba 0.54.1 requires numpy<1.21,>=1.17, but you have numpy 1.24.3 which is incompatible.\n",
      "pandas-profiling 3.3.0 requires joblib~=1.1.0, but you have joblib 1.2.0 which is incompatible.\n",
      "pandas-profiling 3.3.0 requires matplotlib<3.6,>=3.2, but you have matplotlib 3.7.1 which is incompatible.\n",
      "pandas-profiling 3.3.0 requires numpy<1.24,>=1.16.0, but you have numpy 1.24.3 which is incompatible.\n",
      "pandas-profiling 3.3.0 requires pandas!=1.4.0,<1.5,>1.1, but you have pandas 2.0.1 which is incompatible.\n",
      "pandas-profiling 3.3.0 requires scipy<1.10,>=1.4.1, but you have scipy 1.10.1 which is incompatible.\n",
      "tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.24.3 which is incompatible.\n",
      "yfinance 0.2.18 requires lxml>=4.9.1, but you have lxml 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed funcy-2.0 joblib-1.2.0 numpy-1.24.3 pyLDAvis-3.4.1 scipy-1.10.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[-16.38581813 -14.38494449 -17.1741472  ...  -5.94311026 -15.03779418\n",
      "  -16.79976862]\n",
      " [-11.4786464  -14.3848572  -17.1739023  ... -11.02594418 -15.03771574\n",
      "  -16.79966382]\n",
      " [-16.38556974 -14.38493887 -17.17404047 ... -11.95275599 -15.03769374\n",
      "  -16.79965237]\n",
      " ...\n",
      " [-16.29838894 -14.38100379 -17.10624661 ... -17.12196824 -14.97853607\n",
      "  -16.75384611]\n",
      " [-16.35099604 -14.38273988 -17.1290448  ... -17.12896903 -14.83534257\n",
      "  -16.73918494]\n",
      " [-16.32037559 -14.38273957 -17.1220364  ... -17.1323193  -14.93444272\n",
      "  -16.7365521 ]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-4476db3626b344b797b192b341db0bc9.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n",
      "/Users/stevenong/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py:230: UserWarning: The memmapped array [[ -8.75862488  -6.75775125  -9.54695395 ...   1.68408298  -7.41060094\n",
      "   -9.17257538]\n",
      " [ -5.63112551  -8.53733631 -11.32638141 ...  -5.17842329  -9.19019485\n",
      "  -10.95214293]\n",
      " [ -8.99060592  -6.98997505  -9.77907665 ...  -4.55779217  -7.64272992\n",
      "   -9.40468855]\n",
      " ...\n",
      " [  0.34730551   2.26469066  -0.46055216 ...  -0.4762738    1.66715838\n",
      "   -0.10815166]\n",
      " [  0.2802665    2.24852266  -0.49778226 ...  -0.49770649   1.79591997\n",
      "   -0.1079224 ]\n",
      " [  0.32150693   2.25914295  -0.48015388 ...  -0.49043678   1.7074398\n",
      "   -0.09466958]] loaded from the file /var/folders/5r/pb9b594x61g0km_3ckhlt22r0000gn/T/joblib_memmapping_folder_10241_d9d22a44c41a4eadbff8494fbff07fd0_1eda94f3b8374f3b82ea278e940d5fe9/10241-140607621034720-875a1163ccd94ac18b8bed43d50d94d8.pkl is not not bytes aligned. This may cause segmentation faults if this memmapped array is used in some libraries like BLAS or PyTorch. To get rid of this warning, regenerate your pickle file with joblib >= 1.2.0. See https://github.com/joblib/joblib/issues/563 for more details\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyLDAvis in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (3.4.1)\n",
      "Requirement already satisfied: numpy>=1.24.2 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (1.24.3)\n",
      "Requirement already satisfied: scipy in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (1.10.1)\n",
      "Requirement already satisfied: pandas>=2.0.0 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (2.0.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (1.2.0)\n",
      "Requirement already satisfied: jinja2 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (2.11.3)\n",
      "Requirement already satisfied: numexpr in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (2.7.3)\n",
      "Requirement already satisfied: funcy in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (2.0)\n",
      "Requirement already satisfied: scikit-learn>=1.0.0 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (1.1.1)\n",
      "Requirement already satisfied: gensim in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (4.2.0)\n",
      "Requirement already satisfied: setuptools in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pyLDAvis) (58.0.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from pandas>=2.0.0->pyLDAvis) (2.8.2)\n",
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      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=1.0.0->pyLDAvis) (2.2.0)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in /Users/stevenong/opt/anaconda3/lib/python3.9/site-packages (from gensim->pyLDAvis) (5.2.1)\n",
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     ]
    }
   ],
   "source": [
    "lda_vis(lda_gen(positive_df['cleaned_comments_nltk']),'pos')\n",
    "lda_vis(lda_gen(negative_df['cleaned_comments_nltk']),'neg')\n",
    "lda_vis(lda_gen(neutral_df['cleaned_comments_nltk']),'neu')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7734332a-a35e-4363-b0a9-e3147884d03c",
   "metadata": {
    "id": "7734332a-a35e-4363-b0a9-e3147884d03c"
   },
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2d3b786-eed9-4cad-8c0b-1b329629bcca",
   "metadata": {
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   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87e458f9-782f-4200-a37b-009c890d6559",
   "metadata": {
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "IiMGj2WlC1uL",
   "metadata": {
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  {
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   "execution_count": null,
   "id": "0eb325dd",
   "metadata": {
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  {
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   "execution_count": null,
   "id": "bb0d823e",
   "metadata": {
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  {
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   "id": "7077301b",
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  {
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  {
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  {
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  {
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  {
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   "id": "14523be6",
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 ],
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