News_Sentiment_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "News Sentiment Analysis:\n",
    "1. The Scatter plot below shows the relastionship between the \"tweet polarity\" and how \"long ago\" it was tweeted for five news sources. The five news sources are as follows; bbc, cbs, CNN, foxnews and nytimes. As you can see it is very scattered, however you can clearly see that majority of the tweets for all five sources tend to be under 0.0, which implies that most tweets have a negative score on the vader sentiment scale. We need to take into account about what is happening today (June 2018) with President Donald Trump and the most recent Travel Ban affecting those underdeveloped countries\n",
    "\n",
    "2. According to the scatterplot CNN's most recent tweet almost reaches a score of -1 on the vader sentiment scale scale. If we take a closer look at CNN's tweet on June 29th, 11:52am, it says the following : \"An Ivy League student activist who fought sexual violence has been accused of sex crimes against children\" This status is broken own into deperate words,its a process called tokenization. You can clearly see all the negative words used in this status. \n",
    "\n",
    "3. Most of the tweets had a compound score of 0, in which they appeared to be clustered together on the line. Implying that all five media sources reporting these tweets at this time did not have a large impact during the analysis  \n",
    "\n",
    "4. Looking at my overrall media sentiment bar graph, you can see that that foxnews has not only the least average tweets, but they seem to be the only with possitive or neutral score on the vader sentiment scale.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import tweepy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import json\n",
    "import time\n",
    "from datetime import datetime\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Twitter API Keys\n",
    "from API_keys 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": {},
   "outputs": [],
   "source": [
    "target_users = [\"@BBCWorld\", \"@CBSNews\", \"@CNN\", \"@FoxNews\", \"@nytimes\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables for holding sentiments\n",
    "source_account = []\n",
    "text = []\n",
    "date = []\n",
    "tweets_ago = []\n",
    "compound_list = []\n",
    "positive_list = []\n",
    "negative_list = []\n",
    "neutral_list = []\n",
    "\n",
    "\n",
    "# Looping through all target users\n",
    "for user in target_users:\n",
    "     # Variable for holding the oldest tweet\n",
    "    oldest_tweet = None\n",
    "\n",
    "    tweet_count = 0\n",
    "    # Get the last 100 tweets\n",
    "    for x in range(5):\n",
    "        # get all tweets from the home feed\n",
    "        public_tweets = api.user_timeline(user, count=100, result_type=\"recent\", max_id=oldest_tweet)\n",
    "        #loop through all tweets\n",
    "        for tweet in public_tweets:\n",
    "            # keep adding the tweet_count\n",
    "            tweet_count +=1\n",
    "            #append values to empty lists\n",
    "            source_account.append(user)\n",
    "            text.append(tweet['text'])\n",
    "            date.append(tweet['created_at'])\n",
    "            tweets_ago.append(tweet_count)\n",
    "            \n",
    "            # Run the Vader Analysis on each tweet\n",
    "            compound = analyzer.polarity_scores(tweet['text'])['compound']\n",
    "            pos = analyzer.polarity_scores(tweet['text'])['pos']\n",
    "            neg = analyzer.polarity_scores(tweet['text'])['neg']\n",
    "            neu = analyzer.polarity_scores(tweet['text'])['neu']\n",
    "            \n",
    "            # append the values to the lists \n",
    "            compound_list.append(compound)\n",
    "            positive_list.append(pos)\n",
    "            negative_list.append(neg)\n",
    "            neutral_list.append(neu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Source Account</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Date</th>\n",
       "      <th>Tweets Ago</th>\n",
       "      <th>Compound Score</th>\n",
       "      <th>Positive Score</th>\n",
       "      <th>Negative Score</th>\n",
       "      <th>Neutral Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@BBCWorld</td>\n",
       "      <td>'Abacha loot' to be given to poor Nigerians ht...</td>\n",
       "      <td>Fri Jun 29 15:44:02 +0000 2018</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4767</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@BBCWorld</td>\n",
       "      <td>😞 https://t.co/Og4YuJNhiD</td>\n",
       "      <td>Fri Jun 29 15:05:15 +0000 2018</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.4767</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@BBCWorld</td>\n",
       "      <td>Ken Isaacs: UN migration agency rejects Trump ...</td>\n",
       "      <td>Fri Jun 29 14:58:28 +0000 2018</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.4939</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@BBCWorld</td>\n",
       "      <td>Saudi rapper Leesa A celebrates end of country...</td>\n",
       "      <td>Fri Jun 29 14:52:34 +0000 2018</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@BBCWorld</td>\n",
       "      <td>What went wrong for Africa at the World Cup? h...</td>\n",
       "      <td>Fri Jun 29 14:45:34 +0000 2018</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.4767</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Source Account                                              Tweet  \\\n",
       "0      @BBCWorld  'Abacha loot' to be given to poor Nigerians ht...   \n",
       "1      @BBCWorld                          😞 https://t.co/Og4YuJNhiD   \n",
       "2      @BBCWorld  Ken Isaacs: UN migration agency rejects Trump ...   \n",
       "3      @BBCWorld  Saudi rapper Leesa A celebrates end of country...   \n",
       "4      @BBCWorld  What went wrong for Africa at the World Cup? h...   \n",
       "\n",
       "                             Date  Tweets Ago  Compound Score  Positive Score  \\\n",
       "0  Fri Jun 29 15:44:02 +0000 2018           1         -0.4767           0.000   \n",
       "1  Fri Jun 29 15:05:15 +0000 2018           2         -0.4767           0.000   \n",
       "2  Fri Jun 29 14:58:28 +0000 2018           3         -0.4939           0.000   \n",
       "3  Fri Jun 29 14:52:34 +0000 2018           4          0.0258           0.174   \n",
       "4  Fri Jun 29 14:45:34 +0000 2018           5         -0.4767           0.000   \n",
       "\n",
       "   Negative Score  Neutral Score  \n",
       "0           0.279          0.721  \n",
       "1           0.608          0.392  \n",
       "2           0.286          0.714  \n",
       "3           0.169          0.657  \n",
       "4           0.256          0.744  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary for the DataFrame\n",
    "tweet_sentiment = {\n",
    "    \"Source Account\": source_account,\n",
    "    \"Tweet\": text,\n",
    "    \"Date\": date,\n",
    "    \"Tweets Ago\": tweets_ago,\n",
    "    \"Compound Score\": compound_list,\n",
    "    \"Positive Score\": positive_list,\n",
    "    \"Negative Score\": negative_list,\n",
    "    \"Neutral Score\":  neutral_list,\n",
    "    \n",
    "}\n",
    "\n",
    "tweets_df = pd.DataFrame(tweet_sentiment, columns = ['Source Account',\n",
    "                                                     'Tweet',\n",
    "                                                     'Date',\n",
    "                                                     'Tweets Ago',\n",
    "                                                     'Compound Score',\n",
    "                                                     'Positive Score',\n",
    "                                                     'Negative Score',\n",
    "                                                     'Neutral Score'])\n",
    "tweets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x222d6c20400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting twitter info by source\n",
    "# locating bbc,cbs,cnn,fo,nyt\n",
    "bbc = tweets_df.loc[tweets_df['Source Account'] == '@BBCWorld']\n",
    "cbs = tweets_df.loc[tweets_df['Source Account'] == '@CBSNews']\n",
    "cnn = tweets_df.loc[tweets_df['Source Account'] == '@CNN']\n",
    "fox = tweets_df.loc[tweets_df['Source Account'] == '@FoxNews']\n",
    "nyt = tweets_df.loc[tweets_df['Source Account'] == '@nytimes']\n",
    "\n",
    "# Plotting Twitter info\n",
    "plt.scatter(bbc['Tweets Ago'], bbc['Compound Score'], c='c', edgecolor='k', label='BBCWorld')\n",
    "plt.scatter(cbs['Tweets Ago'], cbs['Compound Score'], c='m', edgecolor='k', label='CBS')\n",
    "plt.scatter(cnn['Tweets Ago'], cnn['Compound Score'], c='r', edgecolor='k', label='CNN')\n",
    "plt.scatter(fox['Tweets Ago'], fox['Compound Score'], c='navy', edgecolor='k', label='Fox')\n",
    "plt.scatter(nyt['Tweets Ago'], nyt['Compound Score'], c='y', edgecolor='k', label='New York Times')\n",
    "# Adjusting dimensions & labels\n",
    "plt.title(f\"Sentiment Analysis of Tweets {datetime.now().strftime('%Y-%m-%d %H:%M')}\")\n",
    "plt.xlabel(\"Tweets Ago\")\n",
    "plt.xlim(105,-5)\n",
    "plt.ylabel(\"Tweet Polarity\")\n",
    "plt.ylim(-1.1,1.1)\n",
    "plt.legend(bbox_to_anchor=(1, 0.95), title='Media Sources', fontsize='medium')\n",
    "plt.grid(True)\n",
    "plt.savefig(\"Images1/Sentiment_Analysis_of_Tweets.png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x222d6d6e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a bar graph to show overall sentiment analysis of 100 tweets per source\n",
    "\n",
    "plt.bar(0, bbc['Compound Score'].mean(), color='c', edgecolor='k', width=1)\n",
    "plt.bar(1, cbs['Compound Score'].mean(), color='m', edgecolor='k', width=1)\n",
    "plt.bar(2, cnn['Compound Score'].mean(), color='r', edgecolor='k', width=1)\n",
    "plt.bar(3, fox['Compound Score'].mean(), color='navy', edgecolor='k', width=1)\n",
    "plt.bar(4, nyt['Compound Score'].mean(), color='y', edgecolor='k', width=1)\n",
    "\n",
    "#adjusting labels\n",
    "plt.title(f\"Overall Media Sentiment {datetime.now().strftime('%Y-%m-%d %H:%M')}\")\n",
    "plt.ylabel(\"Tweet Polarity\")\n",
    "plt.ylim(-1,1)\n",
    "\n",
    "x_labels = [\"BBCWorld\", \"CBS\", \"CNN\", \"Fox\", \"NYT\"]\n",
    "\n",
    "x_locations = [value for value in np.arange(5)]\n",
    "plt.xticks(x_locations, x_labels)\n",
    "\n",
    "for a,b in zip(np.arange(5), [bbc['Compound Score'].mean(),\n",
    "                              cbs['Compound Score'].mean(),\n",
    "                              cnn['Compound Score'].mean(),\n",
    "                             fox['Compound Score'].mean(),\n",
    "                              nyt['Compound Score'].mean()]):\n",
    "    if b > 0:\n",
    "        plt.text(a-0.15,b-b-0.25,str(round(b,3)))\n",
    "    else:\n",
    "        plt.text(a-0.15,b-b-0.25,str(round(b,3)))\n",
    "\n",
    "\n",
    "plt.savefig(\"Images1/Overall_Media_Sentiment.png\", bbox_inches=\"tight\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()\n"
   ]
  }
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