News_Mood.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "## The Mood of the News\n",
    "---------\n",
    "\n",
    "What's the mood of the news on December 1, 2017? \n",
    "Lets find out by using VADER (Valence Aware Dictionary for sEntiment Reasoning) sentiment analysis of the Twitter activity of some common news outlets.\n",
    "\n",
    "--------\n",
    "\n",
    "### Analysis\n",
    "-------\n",
    "\n",
    "- The majority of the compound scores for positive, negative and neutral tweets from Twitter for all of the news sources are \"0\" or close to \"0\" as shown in the first graph.  \n",
    "- Overall, the tweets from the 5 news outlets are not favorable on Twitter for December 1, 2017.\n",
    "- However, @CNNbrk seems to be more favorable on Twitter with @BBCNews a close second. Whereas @FoxNews has the least favorable responses on Twitter with @CBSNews a close second as shown in the bar graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dependencies\n",
    "import pandas as pd\n",
    "import tweepy\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import and Initialize Sentiment Analyzer\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Twitter API Keys\n",
    "from config import (consumer_key, \n",
    "                    consumer_secret, \n",
    "                    access_token, \n",
    "                    access_token_secret)\n",
    "\n",
    "# Setup Tweepy API Authentication\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth, parser=tweepy.parsers.JSONParser())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Target Search Term\n",
    "target_user = (\"@BBCNews\", \"@CNNbrk\", \"@CBSNews\",\"@FoxNews\", \"@nytimes\")\n",
    "\n",
    "# Array to hold sentiments\n",
    "sentiments = []\n",
    "\n",
    "# Convert tweet timestamps to datetime objects that can be manipulated by Python\n",
    "converted_timestamps = []\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list = []\n",
    "\n",
    "# Counter\n",
    "counter = 1\n",
    "\n",
    "# Loop through all target users\n",
    "for target in target_user:\n",
    "\n",
    "    # Variable for holding the oldest tweet\n",
    "    oldest_tweet = None\n",
    "\n",
    "    # Variables for holding sentiments\n",
    "    compound_list = []\n",
    "    positive_list = []\n",
    "    neutral_list = []\n",
    "    negative_list = []\n",
    "    \n",
    "    \n",
    "    # Get all tweets from home feed\n",
    "    public_tweets = api.user_timeline(target, count=100, result_type=\"recent\", max_id=oldest_tweet)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets:\n",
    "            \n",
    "        #Convert the raw tweet time to correct datetime stamp.\n",
    "        converted_time = datetime.strptime(tweet[\"created_at\"], \"%a %b %d %H:%M:%S %z %Y\")\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results[\"compound\"]\n",
    "        pos = results[\"pos\"]\n",
    "        neu = results[\"neu\"]\n",
    "        neg = results[\"neg\"]\n",
    "    \n",
    "        # Add sentiments for each tweet into an array\n",
    "        sentiments.append({\"News Source\": target,\n",
    "                       \"Date/Time\": converted_time, \n",
    "                        \"Compound\": compound,\n",
    "                        \"Positive\": pos,\n",
    "                        \"Neutral\": neu,\n",
    "                        \"Negative\": neg,\n",
    "                        \"Tweet\": tweet[\"text\"],\n",
    "                        \"Tweets Ago\": counter})\n",
    "        # Add to counter \n",
    "        counter = counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound</th>\n",
       "      <th>Date/Time</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>News Source</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Tweets Ago</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2017-12-01 22:34:03+00:00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>@BBCNews</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saturday's Times: \"Cabinet split over Green af...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.6908</td>\n",
       "      <td>2017-12-01 22:14:59+00:00</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.701</td>\n",
       "      <td>@BBCNews</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saturday's Daily Star: \"Murder on the danceflo...</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2017-12-01 22:14:54+00:00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>@BBCNews</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saturday's Daily Telegraph: \"Trump aide turns ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2017-12-01 22:14:54+00:00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>@BBCNews</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saturday's Sun: \"Mega star!\" #tomorrowspaperst...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2017-12-01 22:14:49+00:00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>@BBCNews</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Saturday's Daily Mirror: \"The minister for por...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Compound                 Date/Time  Negative  Neutral News Source  \\\n",
       "0    0.0000 2017-12-01 22:34:03+00:00     0.000    1.000    @BBCNews   \n",
       "1   -0.6908 2017-12-01 22:14:59+00:00     0.299    0.701    @BBCNews   \n",
       "2    0.0000 2017-12-01 22:14:54+00:00     0.000    1.000    @BBCNews   \n",
       "3    0.0000 2017-12-01 22:14:54+00:00     0.000    1.000    @BBCNews   \n",
       "4    0.0000 2017-12-01 22:14:49+00:00     0.000    1.000    @BBCNews   \n",
       "\n",
       "   Positive                                              Tweet  Tweets Ago  \n",
       "0       0.0  Saturday's Times: \"Cabinet split over Green af...           1  \n",
       "1       0.0  Saturday's Daily Star: \"Murder on the danceflo...           2  \n",
       "2       0.0  Saturday's Daily Telegraph: \"Trump aide turns ...           3  \n",
       "3       0.0  Saturday's Sun: \"Mega star!\" #tomorrowspaperst...           4  \n",
       "4       0.0  Saturday's Daily Mirror: \"The minister for por...           5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Need to put tweet in df with time stamp, sentiment analysis and tweet. \n",
    "sentiments_df = pd.DataFrame.from_dict(sentiments)\n",
    "sentiments_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>News Source</th>\n",
       "      <th>Date/Time</th>\n",
       "      <th>Compound</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Tweets Ago</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>@BBCNews</td>\n",
       "      <td>2017-12-01 22:34:03+00:00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Saturday's Times: \"Cabinet split over Green af...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@BBCNews</td>\n",
       "      <td>2017-12-01 22:14:59+00:00</td>\n",
       "      <td>-0.6908</td>\n",
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       "      <td>0.299</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>@BBCNews</td>\n",
       "      <td>2017-12-01 22:14:54+00:00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Saturday's Daily Telegraph: \"Trump aide turns ...</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@BBCNews</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Saturday's Sun: \"Mega star!\" #tomorrowspaperst...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@BBCNews</td>\n",
       "      <td>2017-12-01 22:14:49+00:00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Saturday's Daily Mirror: \"The minister for por...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  News Source                 Date/Time  Compound  Positive  Neutral  \\\n",
       "0    @BBCNews 2017-12-01 22:34:03+00:00    0.0000       0.0    1.000   \n",
       "1    @BBCNews 2017-12-01 22:14:59+00:00   -0.6908       0.0    0.701   \n",
       "2    @BBCNews 2017-12-01 22:14:54+00:00    0.0000       0.0    1.000   \n",
       "3    @BBCNews 2017-12-01 22:14:54+00:00    0.0000       0.0    1.000   \n",
       "4    @BBCNews 2017-12-01 22:14:49+00:00    0.0000       0.0    1.000   \n",
       "\n",
       "   Negative                                              Tweet  Tweets Ago  \n",
       "0     0.000  Saturday's Times: \"Cabinet split over Green af...           1  \n",
       "1     0.299  Saturday's Daily Star: \"Murder on the danceflo...           2  \n",
       "2     0.000  Saturday's Daily Telegraph: \"Trump aide turns ...           3  \n",
       "3     0.000  Saturday's Sun: \"Mega star!\" #tomorrowspaperst...           4  \n",
       "4     0.000  Saturday's Daily Mirror: \"The minister for por...           5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make the df look nice.  \n",
    "news_sentiments = pd.DataFrame(sentiments_df, columns= [\"News Source\", \"Date/Time\", \"Compound\",\"Positive\",\n",
    "                                                        \"Neutral\", \"Negative\", \"Tweet\", \"Tweets Ago\"])\n",
    "news_sentiments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Export the dataframe to a csv file. \n",
    "news_sentiments.to_csv('New_Source_sentiments.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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JvAVVRXWx7goEZUXsHhUIyE9/kygrIv2NTI61tUh/U1ZEMm9BVXDx4gW8B7pz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116dc7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graph the 100 tweets from each news source with a timestamp \"Tweets Ago\". \n",
    "\n",
    "colors = ['magenta','red','green','yellow','orange']\n",
    "plt.style.use('seaborn')\n",
    "for i in np.arange(0,len(target_user)):\n",
    "    news_source = news_sentiments.loc[news_sentiments[\"News Source\"]==target_user[i]]\n",
    "    news_source = news_source.sort_values(\"Tweets Ago\")\n",
    "    plt.scatter(np.arange(len(news_source[\"Compound\"])), \n",
    "            news_source[\"Compound\"], color = colors[i],\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8,label= target_user[i])\n",
    "\n",
    "plt.title(f\"Sentiment Analysis of Tweets {datetime.now().strftime('%Y-%m-%d %H:%M')}\")\n",
    "plt.ylabel(\"Tweet Polarity\")\n",
    "plt.xlabel(\"Tweets Ago\")\n",
    "plt.grid(True)\n",
    "plt.legend(bbox_to_anchor=(1.05,1),loc=2, borderaxespad = 0.,title=\"News Source\")\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig(\"News_Tweets.png\", bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hatas+Vrj8jKFQpHl6urX4eHhga1btxqvfp4/f36WzyxzWm9OZ3dedenSJeh0OlSuXBkn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\n2QMA4DjKHgAAxz0AYXD7kyn5Tx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180b09e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a bar graph showing the overall sentiment analysis of the 100 tweets for each news source.\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "fig, ax = plt.subplots()\n",
    "tick_locations = []\n",
    "news_avg_cmpd = {}\n",
    "\n",
    "for i in np.arange(0,len(target_user)):\n",
    "    news_source = news_sentiments.loc[news_sentiments[\"News Source\"]==target_user[i]]\n",
    "    news_avg_cmpd[target_user[i]] = news_source[\"Compound\"].mean()\n",
    "    tick_locations.append(i + 0.4)\n",
    "\n",
    "x_axis = np.arange(0, len(target_user) )\n",
    "plt.xlim(-0.25, len(target_user))\n",
    "plt.ylim(min(news_avg_cmpd.values()) - 0.005, max(news_avg_cmpd.values()) + 0.1)\n",
    "plt.title(f\"Overall Media Sentiment Based on Twitter {datetime.now().strftime('%Y-%m-%d %H:%M')}\" )\n",
    "plt.ylabel(\"Tweet Polarity\")\n",
    "plt.xticks(tick_locations, news_avg_cmpd, rotation=\"horizontal\")\n",
    "bar_graphs = plt.bar(x_axis, news_avg_cmpd.values(), color = colors, alpha=0.75, align=\"edge\")\n",
    "\n",
    "def bar_label(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x() + rect.get_width()/2., 0.9*height,\n",
    "                '-%.3f' %float(height), ha='center', va='bottom', fontsize=12)\n",
    "\n",
    "bar_label(bar_graphs)\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig(\"Overall_Media_Sentiment.png\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}