Sentiment_Analysis_of_Tweets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb6fa1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import spacy\n",
    "from spacy.lang.en.stop_words import STOP_WORDS as en_stop\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd98a6a7",
   "metadata": {},
   "source": [
    "# 1. Rendering Sentiment Scores Using Previously Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9957bff9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Import df with cleaned Steelers tweets\n",
    "df = pd.read_pickle(\"game_df_clean.pkl\")\n",
    "\n",
    "#Define a function with additional cleaning steps needed to make Steelers tweets compatible with data that trained the model\n",
    "def text_cleaner_2(text):\n",
    "    \n",
    "    #remove stop words\n",
    "    stop_words = set(en_stop)\n",
    "    tokens = word_tokenize(text)\n",
    "    tokenized = [w for w in tokens if not w in stop_words]\n",
    "    text = ' '.join(tokenized)\n",
    "    \n",
    "    #remove unhelpful words\n",
    "    unhelpful_words = ['like', 'day', 'time', 'quot', 'day', 'feel', 'today', 'got']\n",
    "    for word in unhelpful_words:\n",
    "        text = text.replace(word, '')\n",
    "    \n",
    "    #apply stemmer\n",
    "    s_stemmer = SnowballStemmer(language='english')\n",
    "    stems=[]\n",
    "    for word in text.split():\n",
    "        stm= s_stemmer.stem(word)\n",
    "        stems.append(stm)\n",
    "        \n",
    "        if len(stems) > 2:\n",
    "            text = ' '.join(stems)\n",
    "    return text\n",
    "\n",
    "df['sentiment_text'] = df.sentiment_text.apply(lambda x: text_cleaner_2(x))\n",
    "\n",
    "#Import trained vectorizer and apply it to Steelers tweets\n",
    "vectorizer=pickle.load(open('tfidf_vectorizer.sav', 'rb'))\n",
    "X= vectorizer.transform(df['sentiment_text'])\n",
    "\n",
    "#Import trained logistic regression model and use it to render sentiment scores\n",
    "tuned_log_model= pickle.load(open('tuned_log_model.sav', 'rb'))\n",
    "threshold = .57\n",
    "X = tuned_log_model.predict_proba(X)[:,1]\n",
    "df['sentiment'] = ['pos' if i > threshold else 'neg' for i in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b0948f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>wish</th>\n",
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      "text/plain": [
       "                  0\n",
       "sad       11.884840\n",
       "miss       7.464209\n",
       "poor       6.890863\n",
       "sick       6.203808\n",
       "depress    5.960013\n",
       "hurt       5.826666\n",
       "wish       5.752008\n",
       "unfortun   5.723859\n",
       "upset      5.687731\n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top Ten Features Used by Model for Prediction\n",
    "coefs = pd.DataFrame(tuned_log_model.coef_, columns= vectorizer.get_feature_names_out()).T\n",
    "abs(coefs).sort_values(by=0, ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82b3693",
   "metadata": {},
   "source": [
    "# 2. Sentiment of Tweets about the Steelers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe65fdda",
   "metadata": {},
   "outputs": [
    {
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      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sentiment.value_counts().plot.bar(title='General Distribution of Sentiment Scores');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9843ee1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sentiment by Quarter\n",
    "sns.catplot(x=\"sentiment\",\n",
    "            col='quarter',\n",
    "            kind=\"count\", data=df,\n",
    "            aspect=0.7);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c47bb0f",
   "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></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quarter</th>\n",
       "      <th>sentiment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">pregame</th>\n",
       "      <th>neg</th>\n",
       "      <td>1371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos</th>\n",
       "      <td>1372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">second</th>\n",
       "      <th>neg</th>\n",
       "      <td>1891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos</th>\n",
       "      <td>1052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">fourth</th>\n",
       "      <th>neg</th>\n",
       "      <td>1750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos</th>\n",
       "      <td>1309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Count\n",
       "quarter sentiment       \n",
       "pregame neg         1371\n",
       "        pos         1372\n",
       "second  neg         1891\n",
       "        pos         1052\n",
       "fourth  neg         1750\n",
       "        pos         1309"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sentiment by Quarter Counts\n",
    "df2 = pd.DataFrame(df.groupby(['quarter', 'sentiment']).sentiment.size().values, \n",
    "                   index=df.groupby(['quarter', 'sentiment']).sentiment.size().index, \n",
    "                   columns=['Count'])\n",
    "df2=df2.reindex(['pregame', 'second', 'fourth'], level=0)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65924c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Breakdown of Sentiment by Hour\n",
    "df_bins = df.copy()\n",
    "df_bins['time'] = pd.to_datetime(df_bins['time'], format=\"%I:%M.%S%p\")\n",
    "\n",
    "bins = [16,17,18,19, 21,22,23]\n",
    "\n",
    "labels = ['4-5pm','5-6pm','6-7pm','7-8pm','9-10pm','10-11pm']\n",
    "df_bins['time_bin'] = pd.cut(df_bins.time.dt.hour,bins, labels=labels, right=False)\n",
    "\n",
    "hour_plot= sns.catplot(x=\"sentiment\",\n",
    "                                    col='time_bin',\n",
    "                                    kind=\"count\", \n",
    "                                    data=df_bins,\n",
    "                                    aspect=0.5)\n",
    "plt.savefig('sentiment_by_hour_plot.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e99c136",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\karbo\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1667: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = value\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sentiment during the Game\n",
    "df_gametime= df_bins[(df_bins['time_bin'] == '9-10pm') | (df_bins['time_bin'] =='10-11pm')]\n",
    "\n",
    "#Create bins for (approximately) every ten minutes\n",
    "labels= ['9:00-9:09pm', '9:10-9:20pm', '9:20-9:30pm', '9:30-9:40pm', '9:40-9:50pm', '9:50-10:00pm',\n",
    "       '10:00-10:10pm', '10:10-10:20pm', '10:20-10:30pm', '10:30-10:40pm', '10:40-10:50pm', '10:50-11:00pm']\n",
    "\n",
    "df_gametime.loc[:, 'time_bin2'] = pd.cut(df_gametime.loc[:, 'time'],labels=labels,bins=12,right=False)\n",
    "\n",
    "game_plot = pd.DataFrame(df_gametime.groupby(['time_bin2', 'sentiment']).sentiment.size().values, \n",
    "                         index=df_gametime.groupby(['time_bin2', 'sentiment']).sentiment.size().index, \n",
    "                         columns=['Count'])\n",
    "\n",
    "line, ax = plt.subplots(figsize=(12,8))\n",
    "sns.lineplot(x= 'time_bin2',\n",
    "             y= 'Count',\n",
    "             data=game_plot,\n",
    "             hue='sentiment',\n",
    "             ci=None\n",
    ")\n",
    "plt.xticks(rotation=45,\n",
    "          horizontalalignment='right');\n",
    "plt.title('Game Time Sentiment Evolution')\n",
    "plt.xlabel('Game Time')\n",
    "plt.savefig('game_sentiment_evolution.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7967fd62",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "Interestingly, before the game starts there's a nearly equal distribution of positive and negative tweets about the Steelers. However, expectedly, as the game progresses, and the team proceeds to get handily beaten by the Chiefs, we see an increase in negative tweets. During the game itself, positive and negative tweets exhibit a decline proceeding to halftime and then spike around halftime and level off. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab321196",
   "metadata": {},
   "source": [
    "# 3. Sentiment about Big Ben"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15e54157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Filter tweets pertaining to Ben Roethlisberger\n",
    "df_Ben = df[df['sentiment_text'].str.contains('Ben|ben|Roethlisberger|roethlisberger|Big|big')]\n",
    "\n",
    "df_Ben.sentiment.value_counts().plot.bar(title='Overall Sentiment about Ben');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48a7b12a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 756x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Breakdown of Sentiment Scores for Ben by Quarter\n",
    "sns.catplot(x=\"sentiment\",\n",
    "            col='quarter',\n",
    "            kind=\"count\", data=df_Ben,\n",
    "            aspect=0.7);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "56f4bf14",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Breakdown of Sentiment Scores about Ben by the Hour\n",
    "Ben_tweets=df_bins[df_bins['sentiment_text'].str.contains('Ben|ben|Roethlisberger|roethlisberger|Big|big')]\n",
    "sns.catplot(x=\"sentiment\",\n",
    "            col='time_bin',\n",
    "            kind=\"count\", data=Ben_tweets,\n",
    "            aspect=0.5)\n",
    "plt.savefig('Ben_sentiment_by_hour.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc854f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ben_game_tweets= df_gametime[df_gametime['sentiment_text'].str.contains('Ben|ben|Roethlisberger|roethlisberger|Big|big')]\n",
    "Ben_game_plot = pd.DataFrame(Ben_game_tweets.groupby(['time_bin2', 'sentiment']).sentiment.size().values, \n",
    "                         index=Ben_game_tweets.groupby(['time_bin2', 'sentiment']).sentiment.size().index, \n",
    "                         columns=['Count'])\n",
    "\n",
    "line, ax = plt.subplots(figsize=(12,8))\n",
    "sns.lineplot( x= 'time_bin2',\n",
    "             y= 'Count',\n",
    "             data=Ben_game_plot,\n",
    "             hue='sentiment',\n",
    "             ci=None\n",
    ")\n",
    "plt.xticks(rotation=45,\n",
    "          horizontalalignment='right')\n",
    "plt.xlabel('Game Time')\n",
    "plt.title('Evolution of Sentiment about Ben during the Game')\n",
    "plt.savefig('Ben_Game_Sentiment_Evolution.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9df4001d",
   "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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fan</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thank</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>playoff</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watch</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qb</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>career</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count\n",
       "fan         29\n",
       "game        26\n",
       "thank       23\n",
       "year        23\n",
       "playoff     18\n",
       "season      15\n",
       "watch       14\n",
       "team        13\n",
       "qb          12\n",
       "career      12"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top Ten words that appear most frequently in the spike of positive tweets near the end of the game\n",
    "\n",
    "final_tweets= Ben_game_tweets[(Ben_game_tweets['time_bin2'] == '10:30-10:40pm')|\n",
    "                              (Ben_game_tweets['time_bin2'] == '10:40-10:50pm')|\n",
    "                              (Ben_game_tweets['time_bin2'] == '10:50-11:00pm')] \n",
    "#Create list of stop words \n",
    "stop_words = list(en_stop) + ['ll','ve', 'ben', 'big','big ben', 'steeler', 'roethlisberg']\n",
    "\n",
    "#Create a document term matrix for the tweets about Ben (including bigrams)\n",
    "cv = CountVectorizer(stop_words=stop_words, \n",
    "                     ngram_range=(1,2))\n",
    "cv_Ben = cv.fit_transform(final_tweets.sentiment_text)\n",
    "dtm_Ben = pd.DataFrame(cv_Ben.toarray(), columns=cv.get_feature_names_out())\n",
    "dtm_Ben= pd.DataFrame(dtm_Ben.sum().values, index=dtm_Ben.sum().index, columns=['count'])\n",
    "dtm_Ben.sort_values(by='count', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7e8b44",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "Overall, there are more negative than positive tweets pertaining to Ben. The distribution is nearly equal before the game. Then during the second quarter the negative tweets far outpace the positive ones. Interestingly, in the last thirty minutes of the sample time span (near the end of the game) there's a spike in positive tweets. Knowing this to be his last game, otherwise critical Steelers fans are paying homage to him and his career.  "
   ]
  }
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