ICT550_Sentiment_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "96729457",
   "metadata": {},
   "source": [
    "# Sentiment Analysis for ICT550 - Principle of Data Management"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52c0fc98",
   "metadata": {},
   "source": [
    "### Source: https://www.kaggle.com/datasets/nelgiriyewithana/mcdonalds-store-reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9bc75ff0-8d42-4bc1-8904-7a7700bce02a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\User'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "094f3933-4bae-4e9c-9c2e-8e820aaa8f6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1e17cf1-95e4-41e5-a97b-c6eede436d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:\\\\Users\\\\User\\\\McDonald.csv',encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "602af8b4-1a7a-443d-8d06-c50a708cce29",
   "metadata": {},
   "outputs": [],
   "source": [
    "print (df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f99282a6-c2ae-47ec-8475-869f4e179d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df .describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "868bdf69-0266-4424-9ded-e5e49d31ff01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the data types\n",
    "numerical_cols = df.select_dtypes(include=['int', 'float']).columns\n",
    "categorical_cols = df.select_dtypes(include=['object', 'category']).columns\n",
    "\n",
    " \n",
    "\n",
    "# Print the numerical and categorical columns\n",
    "print(\"Numerical Columns:\")\n",
    "\n",
    "print (numerical_cols)\n",
    "\n",
    "print(\"\\nCategorical Columns:\")\n",
    "\n",
    "print (categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f02ef61e-c7a7-4fcb-b743-b5443f685a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.astype('object').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "327a8d18-9689-4408-b294-425ca0866d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install pandas-profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7391127-5752-4fb5-a498-f0883fcce79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ydata_profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e405d37-6b01-4fba-8da3-d91f7a6cc481",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Types:\n",
      "reviewer_id        int64\n",
      "store_name        object\n",
      "category          object\n",
      "store_address     object\n",
      "latitude         float64\n",
      "longitude        float64\n",
      "rating_count      object\n",
      "review_time       object\n",
      "review            object\n",
      "rating            object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Check data types\n",
    "print(\"Data Types:\")\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9c2617c6-a397-44f3-b7df-d2cc6c39e6d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values:\n",
      "reviewer_id        0\n",
      "store_name         0\n",
      "category           0\n",
      "store_address      0\n",
      "latitude         660\n",
      "longitude        660\n",
      "rating_count       0\n",
      "review_time        0\n",
      "review             0\n",
      "rating             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(\"Missing Values:\")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f347e23-a48f-438b-b2ba-9cd8a8c69545",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rating'] = df['rating'].str.replace('star', '')\n",
    "df['rating'] = df['rating'].str.replace('stars', '')\n",
    "df['rating'] = df['rating'].str.replace('s', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6e5c52c1-01ff-414f-b111-d9b8955bc287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1 \n",
       "1    4 \n",
       "2    1 \n",
       "3    5 \n",
       "4    1 \n",
       "Name: rating, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate = df[\"rating\"]\n",
    "rate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "681632c2-879f-4080-900c-ee27423287cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rating'] = df['rating'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6ea10b56-5579-420b-bb89-2215f8321589",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [reviewer_id, store_name, category, store_address, latitude , longitude, rating_count, review_time, review, rating]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "def check_value_ranges(df):\n",
    "    invalid_rating = df[(df['rating']<1) | (df['rating']>5)]\n",
    "    return invalid_rating\n",
    "\n",
    "invalid_rating = check_value_ranges(df)\n",
    "print(invalid_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48388b72-8859-4f6e-96a6-4a7ea2b9b5f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Columns:\n",
      "Index(['reviewer_id', 'latitude ', 'longitude', 'rating'], dtype='object')\n",
      "\n",
      "Categorical Columns:\n",
      "Index(['store_name', 'category', 'store_address', 'rating_count',\n",
      "       'review_time', 'review'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Determine the data types\n",
    "numerical_cols = df.select_dtypes(include=['int', 'float']).columns\n",
    "categorical_cols = df.select_dtypes(include=['object', 'category']).columns\n",
    "\n",
    "# Print the numerical and categorical columns\n",
    "print(\"Numerical Columns:\")\n",
    "print(numerical_cols)\n",
    "\n",
    "print(\"\\nCategorical Columns:\")\n",
    "print(categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8063c69-0daf-4581-a6d0-2027d1069227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reviewer_id        0\n",
       "store_name         0\n",
       "category           0\n",
       "store_address      0\n",
       "latitude         660\n",
       "longitude        660\n",
       "rating_count       0\n",
       "review_time        0\n",
       "review             0\n",
       "rating             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "947b9b0b-8f0e-4e7d-b53f-6ef67b8f8b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'latitude ':'latitude'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47313b48-1577-4399-8094-f221bf3334ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['reviewer_id', 'store_name', 'category', 'store_address', 'latitude',\n",
      "       'longitude', 'rating_count', 'review_time', 'review', 'rating'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6834e640-d3ea-4b7d-bd04-458871439fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in the ‘latitude’ and 'longtitude' column with a default value, which is '0'\n",
    "df['latitude'].fillna('0', inplace=True)\n",
    "df['longitude'].fillna('0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16a2c745-b002-4b29-a8f6-a6d790f75fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reviewer_id      0\n",
       "store_name       0\n",
       "category         0\n",
       "store_address    0\n",
       "latitude         0\n",
       "longitude        0\n",
       "rating_count     0\n",
       "review_time      0\n",
       "review           0\n",
       "rating           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3dce307-35a0-44d0-bcd0-e8e4aa281524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        1.000000\n",
      "1        2.500000\n",
      "2        2.000000\n",
      "3        3.333333\n",
      "4        2.333333\n",
      "           ...   \n",
      "33391    3.333333\n",
      "33392    3.666667\n",
      "33393    3.333333\n",
      "33394    4.666667\n",
      "33395    4.666667\n",
      "Name: smoothed_rating, Length: 33396, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def smooth_data(df,window=3):\n",
    "    smoothed_data = df['rating'].rolling(window=window, min_periods=1).mean()\n",
    "    return smoothed_data\n",
    "\n",
    "df['smoothed_rating']= smooth_data(df)\n",
    "print(df['smoothed_rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f0c350ae-604f-464e-b792-443142df5483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sentiment_counts = df['review'].value_counts()[:100]\n",
    "labels = sentiment_counts.index\n",
    "sizes = sentiment_counts.values\n",
    "\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.title('Sentiment Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a8a0415-58cf-4d79-895c-de95610ce542",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Polarity Rating'] = df['rating'].apply(lambda x: 'Positive' if x > 3 else('Neutral' if x == 3 else 'Negative'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d4d7ad3b-735e-49b8-8f71-8368bc3fa0a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Positive = df[df['Polarity Rating'] == 'Positive'][0:8000]\n",
    "df_Neutral = df[df['Polarity Rating'] == 'Neutral']\n",
    "df_Negative = df[df['Polarity Rating'] == 'Negative']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "11c9dc36-54aa-4f28-b897-271e564d5607",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Neutral_over = df_Neutral.sample(8000, replace=True)\n",
    "df_Negative_over = df_Negative.sample(8000, replace=True)\n",
    "df = pd.concat([df_Positive, df_Neutral_over, df_Negative_over], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aac94f78-b87c-4b13-aa7b-84320f6fa89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_text_processing(text):\n",
    "    stpword = stopwords .words('english')\n",
    "    no_punctuation = [char for char in text if char not in string.punctuation]\n",
    "    no_punctuation = ''.join(no_punctuation)\n",
    "    return ' '.join([word for word in no_punctuation.split() if word.lower() not in stpword])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2d4a176a-d0ab-4751-bd34-3a167fbe1f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>store_name</th>\n",
       "      <th>category</th>\n",
       "      <th>store_address</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>review_time</th>\n",
       "      <th>review</th>\n",
       "      <th>rating</th>\n",
       "      <th>smoothed_rating</th>\n",
       "      <th>Polarity Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>5 days ago</td>\n",
       "      <td>It'd McDonalds. It is what it is as far as the...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>a month ago</td>\n",
       "      <td>My mc. Crispy chicken sandwich was ï¿½ï¿½ï¿½ï¿...</td>\n",
       "      <td>5</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>3 months ago</td>\n",
       "      <td>I'm not really a huge fan of fast food, but I ...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>a year ago</td>\n",
       "      <td>The line wS extremely big and was moving very ...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>7 months ago</td>\n",
       "      <td>The staff seems to always be different which m...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27926</th>\n",
       "      <td>27927</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>5725 W Irlo Bronson Memorial Hwy, Kissimmee, F...</td>\n",
       "      <td>28.333508</td>\n",
       "      <td>-81.513738</td>\n",
       "      <td>5,567</td>\n",
       "      <td>a year ago</td>\n",
       "      <td>Food was cold. Cheese wasn't melted on burger....</td>\n",
       "      <td>1</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23166</th>\n",
       "      <td>23167</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>5725 W Irlo Bronson Memorial Hwy, Kissimmee, F...</td>\n",
       "      <td>28.333508</td>\n",
       "      <td>-81.513738</td>\n",
       "      <td>5,566</td>\n",
       "      <td>7 years ago</td>\n",
       "      <td>I work at Ponderosa Steakhouse next door to th...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14941</th>\n",
       "      <td>14942</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>111 Madison St, Oak Park, IL 60302, United States</td>\n",
       "      <td>41.879656</td>\n",
       "      <td>-87.777913</td>\n",
       "      <td>1,454</td>\n",
       "      <td>5 years ago</td>\n",
       "      <td>Sandwich Lukewarm,clients are loud and childre...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7761</th>\n",
       "      <td>7762</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>11382 US-441, Orlando, FL 32837, United States</td>\n",
       "      <td>28.399986</td>\n",
       "      <td>-81.405103</td>\n",
       "      <td>1,861</td>\n",
       "      <td>a month ago</td>\n",
       "      <td>As we walk in a girl behind the counter identi...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32366</th>\n",
       "      <td>32367</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>3501 Biscayne Blvd, Miami, FL 33137, United St...</td>\n",
       "      <td>25.81</td>\n",
       "      <td>-80.189098</td>\n",
       "      <td>2,810</td>\n",
       "      <td>2 years ago</td>\n",
       "      <td>I move close to this unit, ordered via app to ...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       reviewer_id  store_name              category  \\\n",
       "1                2  McDonald's  Fast food restaurant   \n",
       "3                4  McDonald's  Fast food restaurant   \n",
       "9               10  McDonald's  Fast food restaurant   \n",
       "16              17  McDonald's  Fast food restaurant   \n",
       "19              20  McDonald's  Fast food restaurant   \n",
       "...            ...         ...                   ...   \n",
       "27926        27927  McDonald's  Fast food restaurant   \n",
       "23166        23167  McDonald's  Fast food restaurant   \n",
       "14941        14942  McDonald's  Fast food restaurant   \n",
       "7761          7762  McDonald's  Fast food restaurant   \n",
       "32366        32367  McDonald's  Fast food restaurant   \n",
       "\n",
       "                                           store_address   latitude  \\\n",
       "1      13749 US-183 Hwy, Austin, TX 78750, United States  30.460718   \n",
       "3      13749 US-183 Hwy, Austin, TX 78750, United States  30.460718   \n",
       "9      13749 US-183 Hwy, Austin, TX 78750, United States  30.460718   \n",
       "16     13749 US-183 Hwy, Austin, TX 78750, United States  30.460718   \n",
       "19     13749 US-183 Hwy, Austin, TX 78750, United States  30.460718   \n",
       "...                                                  ...        ...   \n",
       "27926  5725 W Irlo Bronson Memorial Hwy, Kissimmee, F...  28.333508   \n",
       "23166  5725 W Irlo Bronson Memorial Hwy, Kissimmee, F...  28.333508   \n",
       "14941  111 Madison St, Oak Park, IL 60302, United States  41.879656   \n",
       "7761      11382 US-441, Orlando, FL 32837, United States  28.399986   \n",
       "32366  3501 Biscayne Blvd, Miami, FL 33137, United St...      25.81   \n",
       "\n",
       "       longitude rating_count   review_time  \\\n",
       "1     -97.792874        1,240    5 days ago   \n",
       "3     -97.792874        1,240   a month ago   \n",
       "9     -97.792874        1,240  3 months ago   \n",
       "16    -97.792874        1,240    a year ago   \n",
       "19    -97.792874        1,240  7 months ago   \n",
       "...          ...          ...           ...   \n",
       "27926 -81.513738        5,567    a year ago   \n",
       "23166 -81.513738        5,566   7 years ago   \n",
       "14941 -87.777913        1,454   5 years ago   \n",
       "7761  -81.405103        1,861   a month ago   \n",
       "32366 -80.189098        2,810   2 years ago   \n",
       "\n",
       "                                                  review  rating  \\\n",
       "1      It'd McDonalds. It is what it is as far as the...       4   \n",
       "3      My mc. Crispy chicken sandwich was ï¿½ï¿½ï¿½ï¿...       5   \n",
       "9      I'm not really a huge fan of fast food, but I ...       4   \n",
       "16     The line wS extremely big and was moving very ...       4   \n",
       "19     The staff seems to always be different which m...       4   \n",
       "...                                                  ...     ...   \n",
       "27926  Food was cold. Cheese wasn't melted on burger....       1   \n",
       "23166  I work at Ponderosa Steakhouse next door to th...       1   \n",
       "14941  Sandwich Lukewarm,clients are loud and childre...       1   \n",
       "7761   As we walk in a girl behind the counter identi...       1   \n",
       "32366  I move close to this unit, ordered via app to ...       1   \n",
       "\n",
       "       smoothed_rating Polarity Rating  \n",
       "1             2.500000        Positive  \n",
       "3             3.333333        Positive  \n",
       "9             2.000000        Positive  \n",
       "16            2.333333        Positive  \n",
       "19            2.000000        Positive  \n",
       "...                ...             ...  \n",
       "27926         2.333333        Negative  \n",
       "23166         2.666667        Negative  \n",
       "14941         2.333333        Negative  \n",
       "7761          1.000000        Negative  \n",
       "32366         2.000000        Negative  \n",
       "\n",
       "[24000 rows x 12 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "72f89539-a8ff-471a-b0e7-e8e094b232ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Polarity_Rating'] = df['rating'].apply(lambda x: 'Positive' if x > 3 else('Neutral' if x == 3 else 'Negative'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5827fe12-59a8-48f6-af6d-5af35bc7812d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='rating', ylabel='count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='rating', data=df, palette='YlGnBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "342f1863-74af-46e4-a8a9-fe39418cf343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Polarity_Rating', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Polarity_Rating',data=df, palette='summer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6ae3311b-14f9-48c2-81b1-e366faebb166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>store_name</th>\n",
       "      <th>category</th>\n",
       "      <th>store_address</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>review_time</th>\n",
       "      <th>review</th>\n",
       "      <th>rating</th>\n",
       "      <th>smoothed_rating</th>\n",
       "      <th>Polarity Rating</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>5 days ago</td>\n",
       "      <td>It'd McDonalds. It is what it is as far as the...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>a month ago</td>\n",
       "      <td>My mc. Crispy chicken sandwich was ï¿½ï¿½ï¿½ï¿...</td>\n",
       "      <td>5</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>3 months ago</td>\n",
       "      <td>I'm not really a huge fan of fast food, but I ...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>a year ago</td>\n",
       "      <td>The line wS extremely big and was moving very ...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>Fast food restaurant</td>\n",
       "      <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
       "      <td>30.460718</td>\n",
       "      <td>-97.792874</td>\n",
       "      <td>1,240</td>\n",
       "      <td>7 months ago</td>\n",
       "      <td>The staff seems to always be different which m...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    reviewer_id  store_name              category  \\\n",
       "1             2  McDonald's  Fast food restaurant   \n",
       "3             4  McDonald's  Fast food restaurant   \n",
       "9            10  McDonald's  Fast food restaurant   \n",
       "16           17  McDonald's  Fast food restaurant   \n",
       "19           20  McDonald's  Fast food restaurant   \n",
       "\n",
       "                                        store_address   latitude  longitude  \\\n",
       "1   13749 US-183 Hwy, Austin, TX 78750, United States  30.460718 -97.792874   \n",
       "3   13749 US-183 Hwy, Austin, TX 78750, United States  30.460718 -97.792874   \n",
       "9   13749 US-183 Hwy, Austin, TX 78750, United States  30.460718 -97.792874   \n",
       "16  13749 US-183 Hwy, Austin, TX 78750, United States  30.460718 -97.792874   \n",
       "19  13749 US-183 Hwy, Austin, TX 78750, United States  30.460718 -97.792874   \n",
       "\n",
       "   rating_count   review_time  \\\n",
       "1         1,240    5 days ago   \n",
       "3         1,240   a month ago   \n",
       "9         1,240  3 months ago   \n",
       "16        1,240    a year ago   \n",
       "19        1,240  7 months ago   \n",
       "\n",
       "                                               review  rating  \\\n",
       "1   It'd McDonalds. It is what it is as far as the...       4   \n",
       "3   My mc. Crispy chicken sandwich was ï¿½ï¿½ï¿½ï¿...       5   \n",
       "9   I'm not really a huge fan of fast food, but I ...       4   \n",
       "16  The line wS extremely big and was moving very ...       4   \n",
       "19  The staff seems to always be different which m...       4   \n",
       "\n",
       "    smoothed_rating Polarity Rating  Negative  Neutral  Positive  \n",
       "1          2.500000        Positive         0        0         1  \n",
       "3          3.333333        Positive         0        0         1  \n",
       "9          2.000000        Positive         0        0         1  \n",
       "16         2.333333        Positive         0        0         1  \n",
       "19         2.000000        Positive         0        0         1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot = pd.get_dummies(df[\"Polarity_Rating\"])\n",
    "df.drop([\"Polarity_Rating\"], axis=1, inplace=True)\n",
    "df = pd.concat([df, one_hot], axis=1)\n",
    "df. head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9a75046d-2622-4f9f-8b50-a2ea67f1ab35",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Determine the frequency of each category\n",
    "category_counts = df['review_time'].value_counts()\n",
    "\n",
    "# Select the top 10 most frequent categories\n",
    "top_categories = category_counts.nlargest (10) . index\n",
    "\n",
    "# Filter the DataFrame based on selected categories\n",
    "filtered_df = df[df['review_time'].isin(top_categories)]\n",
    "\n",
    "# Plot the filtered DataFrame\n",
    "\n",
    "sns.catplot(x=\"review_time\", kind=\"count\", palette=\"ch:.100\", data=filtered_df, height=5, aspect=3.4)\n",
    "plt.title(\"Total Respondents According to Location (Top 10)\")\n",
    "\n",
    "plt.xlabel(\"Review Time\")\n",
    "\n",
    "plt.ylabel(\"Number of Respondents\")\n",
    "\n",
    "plt. show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1041cfa-181a-48ee-ac01-68aeb281529e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}