twitter_sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d945f656",
   "metadata": {},
   "source": [
    "### Project Description:\n",
    "For this project I have downloaded a sample dataset of tweets determined to be made by Russian troll accounts. The data was published by FiveThirtyEight and can be found here [3 million Russian troll tweets](https://github.com/fivethirtyeight/russian-troll-tweets)\n",
    "\n",
    "In order to create a sentiment analysis of the tweets present in the csv, we must first import them and get rid of any non-english content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"We have a sitting Democrat US Senator on tria...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Marshawn Lynch arrives to game in anti-Trump s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Daughter of fallen Navy Sailor delivers powerf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JUST IN: President Trump dedicates Presidents ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19,000 RESPECTING our National Anthem! #StandF...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content\n",
       "0  \"We have a sitting Democrat US Senator on tria...\n",
       "1  Marshawn Lynch arrives to game in anti-Trump s...\n",
       "2  Daughter of fallen Navy Sailor delivers powerf...\n",
       "3  JUST IN: President Trump dedicates Presidents ...\n",
       "4  19,000 RESPECTING our National Anthem! #StandF..."
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from textblob import TextBlob\n",
    "import re\n",
    "\n",
    "# Read the content of the tweets from the csv\n",
    "df = pd.read_csv('IRAhandle_tweets_1.csv',usecols=['content','language'])\n",
    "\n",
    "# Remove non-english content from the dataframe\n",
    "df.drop(df.index[df['language']!='English'],inplace=True)\n",
    "df.drop(columns=['language'],inplace=True)\n",
    "df.reset_index(drop=True,inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699a4b0b",
   "metadata": {},
   "source": [
    "We will then attempt to read the data and generate a sentiment analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "7f5a66cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of 190252 tweets:\n",
      "Positive tweets: 56311 - 29.60%\n",
      "Neutral tweets: 94335 - 49.58%\n",
      "Negative tweets: 39606 - 20.82%\n"
     ]
    }
   ],
   "source": [
    "amount = len(df['content'])\n",
    "\n",
    "# Remove tags hashtags and links from the tweet to get a more accurate analysis of the text\n",
    "df['content'].str.replace(r'(@\\w+)|(https?:\\/\\/.+)|(# ?\\w+)','',regex=True)\n",
    "\n",
    "# Check polarity of each tweet\n",
    "df['polarity'] = df['content'].apply(lambda tweet: TextBlob(tweet).polarity)\n",
    "\n",
    "positive = len(df[df['polarity']>0])\n",
    "neutral = len(df[df['polarity']==0])\n",
    "negative = len(df[df['polarity']<0])\n",
    "\n",
    "print(f'Of {amount} tweets:')\n",
    "print('Positive tweets: {} - {:.2f}%'.format(positive,(positive/amount)*100))\n",
    "print('Neutral tweets: {} - {:.2f}%'.format(neutral,(neutral/amount)*100))\n",
    "print('Negative tweets: {} - {:.2f}%'.format(negative,(negative/amount)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821d0532",
   "metadata": {},
   "source": [
    "We can calculate popular keywords in the dataset using spacy. Only the first 10000 tweets will be used to conserve memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "d4d6149b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top keywords:  (Democrat, US, Marshawn Lynch, Lynch, Navy, NFL, BoycottNFL, Trump, Florida, Texas)\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "keywords = nlp(' '.join(df['content'].head(10000)))\n",
    "print('Top keywords: ',keywords.ents[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3917f969",
   "metadata": {},
   "source": [
    "Visualizing our data outputs with matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "328e5106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "labels = 'positive','neutral','negative'\n",
    "fig,ax = plt.subplots()\n",
    "ax.pie([positive,neutral,negative], labels=labels, autopct='%1.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "353a3b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.title('Top 10 Most Used Words')\n",
    "words = [x for x in map(str,keywords.ents) if len(x)>1]\n",
    "df = pd.DataFrame(words,columns=['keywords'])\n",
    "df['keywords'].value_counts()[:10].plot(kind='bar',xlabel='keywords',ylabel='frequency')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "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": "3.9.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "bf55b9dc13acfcbacbb477694459a9d5e8d73a549c21d7d0f3557ad4815a72f8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}