Text Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "# for nlp\n",
    "import nltk\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "# for stemming\n",
    "from nltk.stem import PorterStemmer\n",
    "stemming = PorterStemmer()\n",
    "\n",
    "# for Lemmatizing\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.stem import WordNetLemmatizer \n",
    "lemma = WordNetLemmatizer()\n",
    "lemmatizing = WordNetLemmatizer()\n",
    "from nltk import FreqDist\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Read dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>tidy_tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "      <td>father dysfunctional selfish drags kids into d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks #lyft credit cause offer wheelchair van...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>bihday majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model take</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society #motivation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49154</th>\n",
       "      <td>49155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>thought factory: left-right polarisation! #tru...</td>\n",
       "      <td>thought factory left right polarisation #trump...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49155</th>\n",
       "      <td>49156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>feeling like a mermaid ð #hairflip #neverre...</td>\n",
       "      <td>feeling mermaid #hairflip #neverready #formal ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49156</th>\n",
       "      <td>49157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#hillary #campaigned today in #ohio((omg)) &amp;am...</td>\n",
       "      <td>#hillary #campaigned #ohio used words assets l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49157</th>\n",
       "      <td>49158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>happy, at work conference: right mindset leads...</td>\n",
       "      <td>work conference right mindset leads culture de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49158</th>\n",
       "      <td>49159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>my   song \"so glad\" free download!  #shoegaze ...</td>\n",
       "      <td>song glad free download #shoegaze #newmusic #n...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49159 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  label                                              tweet  \\\n",
       "0          1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1          2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2          3    0.0                                bihday your majesty   \n",
       "3          4    0.0  #model   i love u take with u all the time in ...   \n",
       "4          5    0.0             factsguide: society now    #motivation   \n",
       "...      ...    ...                                                ...   \n",
       "49154  49155    NaN  thought factory: left-right polarisation! #tru...   \n",
       "49155  49156    NaN  feeling like a mermaid ð #hairflip #neverre...   \n",
       "49156  49157    NaN  #hillary #campaigned today in #ohio((omg)) &am...   \n",
       "49157  49158    NaN  happy, at work conference: right mindset leads...   \n",
       "49158  49159    NaN  my   song \"so glad\" free download!  #shoegaze ...   \n",
       "\n",
       "                                              tidy_tweet  \n",
       "0      father dysfunctional selfish drags kids into d...  \n",
       "1      thanks #lyft credit cause offer wheelchair van...  \n",
       "2                                         bihday majesty  \n",
       "3                                            #model take  \n",
       "4                         factsguide society #motivation  \n",
       "...                                                  ...  \n",
       "49154  thought factory left right polarisation #trump...  \n",
       "49155  feeling mermaid #hairflip #neverready #formal ...  \n",
       "49156  #hillary #campaigned #ohio used words assets l...  \n",
       "49157  work conference right mindset leads culture de...  \n",
       "49158  song glad free download #shoegaze #newmusic #n...  \n",
       "\n",
       "[49159 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data_cleaned.csv',index_col=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hashtag analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This includes:\n",
    "\n",
    "    - Extract hashtags\n",
    "    - Most common hashtags\n",
    "    - Most common hatred hashtags\n",
    "    - Most common non-hatred hashtags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Objective**\n",
    "\n",
    "Hashtags in twitter are synonymous with the ongoing trends on twitter at any particular point in time. We should try to check whether these hashtags add any value to our sentiment analysis task, i.e., they help in distinguishing tweets into the different sentiments. The tweet seems interesting in nature and the hashtags in the tweet convey the same feeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will store all the trend terms in two separate lists — one for non-racist/sexist tweets and the other for racist/sexist tweets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Extract hashtags**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hashtag(tweet):\n",
    "    tweets = \" \".join(filter(lambda x: x[0]== '#', tweet.split())) #filter those start with (#)\n",
    "    tweets = re.sub('[^a-zA-Z]',' ',  tweets) #keep only alphabetics\n",
    "    tweets = tweets.lower() #convert to lowercase\n",
    "    tweets = [lemma.lemmatize(word) for word in tweets] #do lemmatization to hashtags\n",
    "    tweets = \"\".join(tweets) #join tweets\n",
    "    return tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\Hp\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('wordnet')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>tidy_tweet</th>\n",
       "      <th>hashtag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "      <td>father dysfunctional selfish drags kids into d...</td>\n",
       "      <td>run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks #lyft credit cause offer wheelchair van...</td>\n",
       "      <td>lyft  disapointed  getthanked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>bihday majesty</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model take</td>\n",
       "      <td>model</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society #motivation</td>\n",
       "      <td>motivation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49154</th>\n",
       "      <td>49155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>thought factory: left-right polarisation! #tru...</td>\n",
       "      <td>thought factory left right polarisation #trump...</td>\n",
       "      <td>trump  uselections      leadership  politics ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49155</th>\n",
       "      <td>49156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>feeling like a mermaid ð #hairflip #neverre...</td>\n",
       "      <td>feeling mermaid #hairflip #neverready #formal ...</td>\n",
       "      <td>hairflip  neverready  formal  wedding  gown  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49156</th>\n",
       "      <td>49157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#hillary #campaigned today in #ohio((omg)) &amp;am...</td>\n",
       "      <td>#hillary #campaigned #ohio used words assets l...</td>\n",
       "      <td>hillary  campaigned  ohio  omg    clinton  ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49157</th>\n",
       "      <td>49158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>happy, at work conference: right mindset leads...</td>\n",
       "      <td>work conference right mindset leads culture de...</td>\n",
       "      <td>work  mindset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49158</th>\n",
       "      <td>49159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>my   song \"so glad\" free download!  #shoegaze ...</td>\n",
       "      <td>song glad free download #shoegaze #newmusic #n...</td>\n",
       "      <td>shoegaze  newmusic  newsong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49159 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  label                                              tweet  \\\n",
       "0          1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1          2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2          3    0.0                                bihday your majesty   \n",
       "3          4    0.0  #model   i love u take with u all the time in ...   \n",
       "4          5    0.0             factsguide: society now    #motivation   \n",
       "...      ...    ...                                                ...   \n",
       "49154  49155    NaN  thought factory: left-right polarisation! #tru...   \n",
       "49155  49156    NaN  feeling like a mermaid ð #hairflip #neverre...   \n",
       "49156  49157    NaN  #hillary #campaigned today in #ohio((omg)) &am...   \n",
       "49157  49158    NaN  happy, at work conference: right mindset leads...   \n",
       "49158  49159    NaN  my   song \"so glad\" free download!  #shoegaze ...   \n",
       "\n",
       "                                              tidy_tweet  \\\n",
       "0      father dysfunctional selfish drags kids into d...   \n",
       "1      thanks #lyft credit cause offer wheelchair van...   \n",
       "2                                         bihday majesty   \n",
       "3                                            #model take   \n",
       "4                         factsguide society #motivation   \n",
       "...                                                  ...   \n",
       "49154  thought factory left right polarisation #trump...   \n",
       "49155  feeling mermaid #hairflip #neverready #formal ...   \n",
       "49156  #hillary #campaigned #ohio used words assets l...   \n",
       "49157  work conference right mindset leads culture de...   \n",
       "49158  song glad free download #shoegaze #newmusic #n...   \n",
       "\n",
       "                                                 hashtag  \n",
       "0                                                    run  \n",
       "1                          lyft  disapointed  getthanked  \n",
       "2                                                         \n",
       "3                                                  model  \n",
       "4                                             motivation  \n",
       "...                                                  ...  \n",
       "49154   trump  uselections      leadership  politics ...  \n",
       "49155   hairflip  neverready  formal  wedding  gown  ...  \n",
       "49156   hillary  campaigned  ohio  omg    clinton  ra...  \n",
       "49157                                      work  mindset  \n",
       "49158                        shoegaze  newmusic  newsong  \n",
       "\n",
       "[49159 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hashtag'] = df.tweet.apply(extract_hashtag)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Most common hashtags**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('love', 2442),\n",
       " ('positive', 1332),\n",
       " ('healthy', 904),\n",
       " ('smile', 856),\n",
       " ('thankful', 724),\n",
       " ('fun', 700),\n",
       " ('life', 677),\n",
       " ('summer', 587),\n",
       " ('model', 576),\n",
       " ('affirmation', 552),\n",
       " ('cute', 543),\n",
       " ('fathersday', 543),\n",
       " ('blog', 524),\n",
       " ('i', 520),\n",
       " ('me', 517),\n",
       " ('am', 477),\n",
       " ('beautiful', 464),\n",
       " ('altwaystoheal', 462),\n",
       " ('gold', 447),\n",
       " ('family', 446)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_all_hashtag = FreqDist(list((\" \".join(df.hashtag)).split())).most_common(20)\n",
    "freq_all_hashtag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "df_allhashtag = pd.DataFrame(freq_all_hashtag, columns=['words', 'frequency'])\n",
    "sns.barplot(x='words', y='frequency', data=df_allhashtag)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title('hashtag words frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Most common hatred hashtags**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('trump', 134),\n",
       " ('politics', 95),\n",
       " ('allahsoil', 92),\n",
       " ('libtard', 76),\n",
       " ('liberal', 75),\n",
       " ('sjw', 74),\n",
       " ('retweet', 63),\n",
       " ('miami', 46),\n",
       " ('black', 44),\n",
       " ('hate', 32),\n",
       " ('tampa', 32),\n",
       " ('s', 28),\n",
       " ('bigot', 28),\n",
       " ('blm', 28),\n",
       " ('hispanic', 28),\n",
       " ('brexit', 27),\n",
       " ('sikh', 27),\n",
       " ('calgary', 27),\n",
       " ('temple', 26),\n",
       " ('wso', 26)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_hatred_hashtag = FreqDist(list((\" \".join(df[df['label']==1]['hashtag'])).split())).most_common(20)\n",
    "freq_hatred_hashtag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "df_hatredhashtag = pd.DataFrame(freq_hatred_hashtag, columns=['words', 'frequency'])\n",
    "sns.barplot(x='words', y='frequency', data=df_hatredhashtag)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title('hatred hashtag frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Most common non-hatred hashtags**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('love', 1580),\n",
       " ('positive', 879),\n",
       " ('smile', 577),\n",
       " ('healthy', 575),\n",
       " ('thankful', 471),\n",
       " ('fun', 463),\n",
       " ('life', 437),\n",
       " ('summer', 392),\n",
       " ('model', 365),\n",
       " ('cute', 364),\n",
       " ('affirmation', 363),\n",
       " ('blog', 352),\n",
       " ('fathersday', 340),\n",
       " ('i', 338),\n",
       " ('me', 336),\n",
       " ('am', 315),\n",
       " ('gold', 301),\n",
       " ('silver', 300),\n",
       " ('altwaystoheal', 295),\n",
       " ('beautiful', 290)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most common non-hatred hashtags\n",
    "freq_non_hatred_hashtag = FreqDist(list((\" \".join(df[df['label']==0]['hashtag'])).split())).most_common(20)\n",
    "freq_non_hatred_hashtag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ErRERwPmVc4wxxgxCelQsku6UdFDVbdUPloiIGQDlf/GyfQzwZOW46WXbmLLceXt3sk4p8t45c+bMGsQ1xhjTW5qxWCYDbwN+J+kSSVu0oJ+jq/RiNtu7JCLOiIgJETFh1KhRtQlnjDGmeXpULBExLSKOBt4OfB84G3hC0hckLdrL6z1d3FuU/2fK9unAUpXjxgJPle1ju9hujDFmkNJUH4ukdwBfB74K/BjYEXgBuKGX17sS2Kss7wVcUdk+WdIIScuSnfR3FHfZi5LWL1bSnpVzjDHGDEKG9XSApLuA54GzgCMi4pWy63ZJG87mvIuBjYDFJE0HjgWOBy6VtC/wBLATQEQ8IOlS4EHgVeCgiHitJHUgGWE2D3BN+RljjBmk9KhYgJ0i4tGudkTEDt2dFBG7dLNrk26OnwpM7WL7ncBqTchpjDFmENCMK2w/SQs3ViQtIum41olkjDFmKNOMYtkqIp5vrETE/wFbt0wiY4wxQ5pmFMuckkY0ViTNA4yYzfHGGGPewjTTx3IhcL2kc8gxJB+mY/S8McYYMws9KpaIOFHSfWSnu4AvRcQvWi6ZMcaYIUkzFgsR4TBfY4wxTdHMXGE7lGnu/yHpBUkvSnphIIQzxhgz9GjGYjkR2C4iHmq1MMYYY4Y+zUSFPW2lYowxplmasVjulPQD4CdAYzoXIuKyVglljDFm6NKMYlkQ+CeweWVbAG8JxfK30/s2ycCSB362ZkmMMWZo0Ey48T4DIYgxxpg3B81Ehb1d0vWS7i/r75Dk5rgxxpguaabz/nvAkcB/ASLiXvKrksYYY8wbaEaxzBsRd3Ta9morhDHGGDP0aUaxPCtpecq35iXtCMxoqVTGGGOGLM1EhR0EnAGsJOmvwGPA7i2VyhhjzJClR4slIh6NiE2BUcBKEfHuiHi8PxeV9ElJD0i6X9LFkuaWtKika8v0MddKWqRy/JGSpkl6RNIW/bm2McaY1tLMN++P6bQOQER8sS8XlDQG+BiwSkT8q3zrfjKwCnB9RBwv6QjgCOBwSauU/asCbwOuk/T2iHitL9c3xhjTWprpY3m58nsN2AoY18/rDgPmkTQMmBd4CphIx3dezgMmleWJwCUR8UpEPAZMA9bt5/WNMca0iGYGSH69ui7pa8CVfb1gRPy1pPEE8C/glxHxS0lLRMSMcswMSYuXU8YAt1WSmF62GWOMGYQ0Y7F0Zl5gub5esPSdTASWJV1b80maXTCAutgW3aQ9RdKdku6cOXNmX0U0xhjTD5rpY7mPjop8TrITv0/9K4VNgcciYmZJ/zLgXcDTkkYXa2U08Ew5fjqwVOX8saTr7A1ExBlkBBsTJkzoUvkYY4xpLc2EG29bWX6VnEa/PwMknwDWlzQv6QrbBLiT7MPZCzi+/F9Rjr8S+L6kb5AWznig84BNY4wxg4RmFMuLndYXbESGAUTEc725YETcLulHwO9JRfUH0sqYH7hU0r6k8tmpHP9AiRx7sBx/kCPCjDFm8NKMYvk96Yr6P7K/Y2Gy4od0kfW6vyUijgWO7bT5FdJ66er4qcDU3l7HGGPMwNNM5/3PyU8TLxYRI0nX2GURsWxE9LkT3xhjzJuTZhTLOhHxs8ZKRFwDvK91IhljjBnKNOMKe7Z8f+VC0vW1O/D3lkpljDFmyNKMxbILGWJ8efmNKtuMMcaYN9DMyPvngI9Lmj8iXhoAmYwxxgxhmvk08bskPUiG+yJpDUmntVwyY4wxQ5JmXGEnAVtQ+lUi4h7gva0UyhhjzNClqbnCIuLJTps8QNEYY0yXNBMV9qSkdwEhaTj5LZWHWiuWMcaYoUozFssB5OeJx5ATQq5Z1o0xxpg3MFuLRdKcwMkRsdsAyWOMMWaIM1uLpUz2OKq4wIwxxpgeaaaP5XHgFklXklPbAxAR32iVUMYYY4Yu3Voski4oizsDPy3HLlD5GWOMMW9gdhbL2pKWIafIP2WA5DHGGDPEmZ1i+Q45Zf6y5BceG4g+fofFGGPMm59uXWER8a2IWBk4JyKWq/z8HRZjjDHd0uM4log4sO6LSlpY0o8kPSzpIUkbSFpU0rWS/lT+F6kcf6SkaZIekbRF3fIYY4ypj6amdGkB3wR+HhErAWuQI/mPAK6PiPHA9WUdSasAk4FVgS2B08r4GmOMMYOQAVcskhYkJ7E8CyAi/hMRzwMTgfPKYecBk8ryROCSiHglIh4DpgHrDqTMxhhjmqcdFstywEzgHEl/kHSmpPmAJSJiBkD5X7wcPwaoToI5vWwzxhgzCGmHYhkGrAWcHhHvJAddHjGb49XFtujyQGmKpDsl3Tlz5sz+S2qMMabXtEOxTAemR8TtZf1HpKJ5WtJogPL/TOX4pSrnjwWe6irhiDgjIiZExIRRo0a1RHhjjDGzZ8AVS0T8jZyKf8WyaRPy65RXAnuVbXsBV5TlK4HJkkZIWhYYD9wxgCIbY4zpBc3MFdYKDgEuKpNbPgrsQyq5SyXtS4723wkgIh6QdCmpfF4FDiqTYw4ZHv72xF6fs9JBV8yy/uvvbdPrNDba/+pen2OMMf2lLYolIu4GJnSxa5Nujp8KTG2lTMYYY+qhXeNYjDHGvEmxYjHGGFMrVizGGGNqxYrFGGNMrVixGGOMqRUrFmOMMbVixWKMMaZWrFiMMcbUihWLMcaYWrFiMcYYUytWLMYYY2rFisUYY0yttGt2YzPA/OicLft03o77/LxmSYwxb3asWEzTfPeCLXp9zkf2+EULJDHGDGbsCjPGGFMrVizGGGNqxa4wM6B8/tLeu9M+/yG704wZSrTNYpE0p6Q/SPppWV9U0rWS/lT+F6kce6SkaZIekdT7mskYY8yA0U5X2MeBhyrrRwDXR8R44PqyjqRVgMnAqsCWwGmS5hxgWY0xxjRJWxSLpLHANsCZlc0TgfPK8nnApMr2SyLilYh4DJgGrDtAohpjjOkl7bJYTgYOA/5X2bZERMwAKP+Ll+1jgCcrx00v24wxxgxCBlyxSNoWeCYi7mr2lC62RTdpT5F0p6Q7Z86c2WcZjTHG9J12RIVtCGwvaWtgbmBBSRcCT0saHREzJI0GninHTweWqpw/Fniqq4Qj4gzgDIAJEyZ0qXzM0GerKz7Y63OumfjjFkhijOmKAbdYIuLIiBgbEePITvkbImJ34Epgr3LYXsAVZflKYLKkEZKWBcYDdwyw2MYYY5pkMI1jOR64VNK+wBPATgAR8YCkS4EHgVeBgyLitfaJaYwxZna0VbFExK+BX5flvwObdHPcVGDqgAlmjDGmzwwmi8WYAWHry4/r03k/+8BnX1/e5rLTe33+1TscOMv6tj+6qNdp/HTH3Xp9jjEDjRWLMUOY7X90Va/PuXLH7VogiTEdeBJKY4wxtWKLxZi3MB/48c19Ou/yD7779eWdL5vW6/N/sMMKs6x/+/Kne53GQR9YotfnmIHBFosxxphasWIxxhhTK3aFGWOGPNf84Nk+nbfVzovVLIkBWyzGGGNqxhaLMcYAfzjzmZ4P6oJ37rf468szTvxrr88ffdibb7J2KxZjjBlEPH1ysxO/d7DEJ9Z+ffmZU3/Zp+sufvDmfTqvK+wKM8YYUytWLMYYY2rFisUYY0ytWLEYY4ypFSsWY4wxtWLFYowxplasWIwxxtTKgCsWSUtJ+pWkhyQ9IOnjZfuikq6V9Kfyv0jlnCMlTZP0iKQtBlpmY4wxzdMOi+VV4NMRsTKwPnCQpFWAI4DrI2I8cH1Zp+ybDKwKbAmcJmnONshtjDGmCQZcsUTEjIj4fVl+EXgIGANMBM4rh50HTCrLE4FLIuKViHgMmAasO6BCG2OMaZq29rFIGge8E7gdWCIiZkAqH6AxAc8Y4MnKadPLNmOMMYOQtikWSfMDPwY+EREvzO7QLrZFN2lOkXSnpDtnzpxZh5jGGGN6SVsUi6S5SKVyUURcVjY/LWl02T8aaEw1Oh1YqnL6WOCprtKNiDMiYkJETBg1alRrhDfGGDNb2hEVJuAs4KGI+EZl15XAXmV5L+CKyvbJkkZIWhYYD9wxUPIaY4zpHe2YNn9DYA/gPkl3l21HAccDl0raF3gC2AkgIh6QdCnwIBlRdlBEvDbgUhtjjGmKAVcsEXEzXfebAGzSzTlTgaktE8oYY0xteOS9McaYWrFiMcYYUytWLMYYY2rFisUYY0ytWLEYY4ypFSsWY4wxtWLFYowxplasWIwxxtSKFYsxxphasWIxxhhTK1YsxhhjasWKxRhjTK1YsRhjjKkVKxZjjDG1YsVijDGmVqxYjDHG1IoVizHGmFqxYjHGGFMrQ0axSNpS0iOSpkk6ot3yGGOM6ZohoVgkzQl8G9gKWAXYRdIq7ZXKGGNMVwwJxQKsC0yLiEcj4j/AJcDENstkjDGmCxQR7ZahRyTtCGwZEfuV9T2A9SLi4E7HTQGmlNUVgUdmk+xiwLP9FG0wpDEYZBgsaQwGGepIYzDIMFjSGAwyDJY0BkqGZSJiVH8uMqw/Jw8g6mLbGzRiRJwBnNFUgtKdETGhX0INgjQGgwyDJY3BIEMdaQwGGQZLGoNBhsGSxmCQoVmGiitsOrBUZX0s8FSbZDHGGDMbhopi+R0wXtKykoYDk4Er2yyTMcaYLhgSrrCIeFXSwcAvgDmBsyPigX4m25TLbAikMRhkGCxpDAYZ6khjMMgwWNIYDDIMljQGgwxNMSQ6740xxgwdhoorzBhjzBDBisUYY0ytWLGYXiGpq9BvYwCQNCT6bfuLpH7XnYPlXWqFHFYsA0RfMq/ODC/RdP05f6ykhSIiWlEQB/olk7RsL45dX9IprZSnDtpdUUkaBVzWbjlahaQlJX0BICL+15/7lKQoHdyS1qhLxl5cf7SkTwF09U5LWkVSn8e7vKUVS/Vh1t3SaqRdKuRhwDy9kGV4teDVIMs7gWP6cf6SwOHAXp2VS3cvV29eOklzVF6y4ZLm7qusTV5vZ+B7kkY0ecpDwMaSvtFCsfpFp4pqwS72L91qGSJiJjkUYDNJ60para60S6W+ZD/OH10UX39YHFhZ0vHQdYXcLJW8mgicLmm+fsrWW5YCNpV0ZEOeyjs9Lzll1sGS1upL4m9pxVLJ3H2BYyUd2J/C2zltSdsC5wEnAkdKGt3VsZ0qhU8CJwE/krRiHbIAM4EPSNqyLydHxN+AO4FlyQlAX1cuFbl3kLRdQ+bevHQR8b+SxseAs4DzJO3UF1l7oqS7G3BARLwyO5eGkjkj4h/AhsCGvbVcJL1f0vaSFq/DfdKdnJV8+ChwgaTjJC1atq0EfEXSga24fpWI+CewMHADsEZDvv6kKelQMkz2MklH9LbhoZwN/TLgx5L27KscEXEvcA6wuqTPl219Vi5FqRwGTImIl+tu3PbAH4DjgDUkHQ0d91Ly8Kdkg2p/SWv3OvWIeEv/gH2Bm4EJwEvAgTWlu3rJvCWB04FrgAUoId7dnLMncD0wAngU+FplX7fnzSa9uYA5y/KHgUPK8hy9SEOV5e3IWaYPABZq7Ad2BR4GfgKcAnywGbnJ+dyWqOTDdeV5XQn8oEX5vTvwP2DP2cnX6b4b9zo/8Bvg1Cav9QngFuAbwP3Aui0uyx8sz/C9wLXACcD+wNVkRX91owy0+gd8Hfg38PZ+pjMJ+EVZPh+4vDfvAjAf2VgZA7wPeA74cB9l2bY8wwuBG3v7fnY+hmys/AM4vjfp9PN5Vsv1MOBdwKXA0dVjgOHAIsDRpf5au1fXGYhCNph+nR7scOBUYAVgj/IyDqvpOhOAo4BNgNuA5cr21YG5yvJawHaVc44G1gcOJhXRXOSA0Dn7cP1VS0X/MfJTA2sCv29U5L1Ma2Pg2LK8PRXlAuwM/IB09c0NHAR8E9ihhzQ3BX4FjC7r+wLjSmV8TSn0w4Cla8qPtwOLluXJZGts885loovzpgAXAEcC65WK6nbgpB6utxLwk7J8UClbc1T211qBAO8sz3Pfsr54yZe/AluQjZXJwHfIFnIr3q3xwKTK+sXAk8CifSnDJY3NgJ2Az5EDpBvvzspNnLsPqeDOBeYp295NWvAfbeL8BYBFyvL8wK+BCWV9XdJ6+UKT91Gtd9YEli/v9rpkA/SQro6tOX8a4xbfBbwH2KCy/kPgyLK+DnATOXXWSDqUy1rNXust5wqLxhNO/+8w4DHyoe0WEZtFjvI/opipvUbSapL2J1/oiSXtSRHxqKStyL6O+cvhqwOfkrR9Wf8X8BWyIt8+Iv4LHAF8oQ/3+QAdo2wvI32qI4A9iounW/O94bKRNIfyWzgjgGUkHRoRV5Iv+Mrki7sysCOwWkT8G/gR8Edgm8p9zZK2pLnIlujNwOKSNiSV+41kYd8qIl4llc2Hy/F9prhSTiZ92UcBvwS+CBwvadtGmagc3/A17002OL5W7nFSRLxMVnZbSjqhcs6wynMbS+b/7yWdTSrjrSI7fHeXNH/na/bhnjrn36vAn4EPSVotIp4p9zgfWQ7/B/wc+A+ws6S9+nP9LuSZq1xnsqSflTx9HriDtKJ6db+SJknanFQEu5ENtW0i4r+SDgG+Kmne7sqxpPeT5fM1YDSwu6RFI+Jm4EPA4ZIWms35C5AW3weKS/EV8j1o9MvdD9wD7CTpxJ7up1LvfAb4KnA82QB7CjgQ2LW47Ohv2ZidDJK2IeuF1YDvStqVbCh9nXT1HkPWiTcCp5GNxe8AT5B11ZrNXuwt9yNbxj8lK9utyLnINir7dgTuBlbsS4uAbMF/vax/hlQs+5GWyz1kJfNusiKdj3Qj/YxUJmOA+8rxS5V9d9ND64w0WRcoy9uUa36ODhfOe0g32y3ANT2ktQrwtrK8VPmfp8j/HeCwsu2EUkAXJiuwuxrPjHyR96cL6whYuJE28H/A05V9vwYuKtebAjwArNTPvN4UuLYs/4R0pzRabnuVZzJfybt16GihDicDFlamYs2Srcw5yNbsuEq+b0U2AD4BfItskX6PfEEbz3P3UgbG9POeqq3fd5Our1Fkuf5sue4aZf9RwNnACmV9+5KPpzbutb9ylHK7SGX7YWTZvw14AfhQL9OdDMwo5ep24HFSue8AfAq4F1h1Nuc3nvMqZX0PshKfAizWKNNNyLE3aZXsVtankNb0amV9M/I9WLPJ+9qQ8v6VPPohxYot+24g3+VWWSzLAb8l+0p3Jy2l6RSXHlnvrFWWR5aydDWwBGkBfxpYsqlrteIGBtuvq4wiW7CXlOUjSgH6BWkCrt6HazRM9HElw3YgK6ztSb/waRS3V3lxxgLzlfW9SoFdj2xJfJ90I1zdKMSzue5w0iL5DB1m9QGkcrmd4v5pyEi6S/bvJq1NgQdJF9dYspW7Zdk3iqw8ryUr3DPISnlc2X8UcGvlZZ6zU9oCliE/0jYfsDTwF1J5NF7ceUkle27Ji1VqyPvtgEOBT5It9hFle6NyWLBy7DlkkMLCZf1Asq/rusoxB9OFG6W8fHcCf6NDwb635P2ZZOU+2wqx2XJc+T+YbIicV9LeGPg48CfSBfU1snL8WJHtM6Q1+T7gqv4834oM25GNiquAsyr75yBdvacDn+5FusuQjbPly/pEUkn8upTrM+m5odVotHyrsm13sq9lnyLb7NyfVZflruT7uDtZMR9EtuiPJRXe+5q8rznId/vL5dxrKmWx4ZKau7/lvQcZFiL7NTcoz/R9wDPAy6Xc/AU4rnL8kqX83kAqlub7Zlt5I4PtB7yDjopvRCn0jQrmbWRlt1iTaS1FUUAls46rpP1+spJavHL8sPLfeCGXLwV0jbK+N1nxNQrZHBSLYzYyNNKaUM49l0qlRyrP3zKrcjkaOLyb9A4hK8DdyD6a3YC/k9bPmeWYK8rLvSjZKX09sGzZd1wphHN1fnEr9z+SrABXKutrlEJ+QOXYOYF5+5nXc5f/lckO95voCGT4OPBjOvzuc1bOu5xUbovQoQiPJF0C3VqQpOI8layEvkmHtbg8sBHZsT6un/e0XGV5hXJPDWton3KffyYbM+eSLc5vkRXaTmRr/x1kA+QuSv9WL2UYXll+X3kebyMt1FfLdavHLE82cHps6ZKV9m1k42a/Sh5OIhXlOj2cfzBpjR1E9lU+BxxR2b8zPfQx0vFOLVUpL5uTlvSupDW9MenZeG+Tz+xdZCNlXClbNwHzl30fJZXmgs2k1cu8atzLimT/17xlfTL5Tr+HbNjdQjZC3k6+i8dW0jiGVMhL9eradd/MYPoxq8tgJBn9cGZ52eYhO6E/18e09yI74eYmW/qfJTuE9y37TgTeUTn+DdqedBn8rnEcHe6qbZq4/jzkl94g+2zWI/sOzqG0uMu+75Kd9sPIr8edSieLrFIAly2VwNN0KNwpZIXxkXLuE8AHKud+m/Q3N1qYI7uQdTGyddfoPD+CjCJbv6y/vzyHT9aU7weSjYajy/px5cX5dLmPe+jCEiRdJt8hrZS7SOW5MRnp9ivSWnuDNUtWWD8sywuT1twZZX0daogGAxYk3XgLlfURpJVwGh2RdecCj1f2r0pWiBtV0tmI7Ntaow8yLEJaQ2uX9XeSjZqtSrldgbSgLqOj4lyHtJLeUC46pT2RfDfHk+/ON4usjQbJZCqKtYvzP0q6HceSCuXLwAfIvq4v9fI+tySt7/PJhsLipYxeQCrwBXqZ3r50BHJ8pLwzJ5GW9H30w4pt4tpbke/n8eVZLFmu+0uywfQyad1tTzaOlivvxwmkUvk1Tbq/Zrluq26o3T9mVSrjygs/L9m6uoKsaE4jQ4zX62O6i5DuqneX9feTbqIfkW6ka7s4ZwvSEmi0lj9Buq8a1s9kmmgdkB3/R5Atj0dJBbca2VH6cSrWDhWXB8X87ibNpci+p3PJSnghsi/hblKhvExGGh1HxQVAtpIvYDYRdaTL5GE6+jAOKgV4vcpzuankU599zGRL9V6yT+iPpJ9+JFnJnFLy/Q0vMtl6e5hi8pOVys0VeUdSXJfdXPdeOlyrS5OtvDtK3i5bU5keUeT8Ulk/s+T9yeW5HUi6M/avnHMycHBlfUX6EWlXyusMOhoew8nGy95l/ZPANEqYMVmRzfb+yT6aJ+iwiucGvlTya7PZlaty/ILlWYwm3X6/IBXCWaSl9ueSf82EBC9Xys17yBb8YaTSXIi0Ur5PsRKbSKvqZr2KrBtENlYOJyv2fvUh9nD9hlU7jgyV/iPpsj2UVHaHkW7Ur5KN7nXKecuQSuUU+tAtEPEmVCydCw/pV76ZNK+/VinwW5SXYAYwtsm056XDungvqVg+WwrNuxrXL4X4JNKSqSqV/UrmXgI8Qkfn+CFki77HlgtZ8e1dlr9FRqt8qrJ/rfJiHc6slktXFlPVRTaZbNXMR5ru3yJ9wfOVAvhySXNOUrEcX57htqQl02PlSbae/kxHZX0I6fvfsPF8+5n3G5GtzV0a91fS/3LlmFlckpXtK5IKdURl262l3CzczfVWZFb31B3A5ZX1PSid5v24p5GV5ZVIK/ke0vW0MOnSe4JU/veTjYrvkArlvaWcbVDDe9VwCy1Gtnb/SodyaQxg3JdstDUdllpJfwcyQqqRd8NIV+vXmikXpNJdA/hVo7yTY0QOpEkLg1RMqwOnVd+ZUr4b/YDdBl6U571HWd6EtLwa4d8bkFZUtXy1eszKIqR78MOlbDYCOLYsz+piytg6so/0CkogAj30Q/V47VbeWDt+lYpjDtKXfEsppEuQleEJzNo5t1CT6c5VMupM0tJ5go7+kcNJ3+n6FP9yySzR4WZalOwAbPjEv0ZWekuX9QOYjalfkWNbstW0fymsx5cXcHM6IsPWI03YcbNJZ+lyL1uV9d2AgxrPkFQa3yjpv51Ukg+TlccSpFK+kmyp9zimoHLdzsrlMLJvYO5+FeR8to+TfT6/ocNdszDpvjmxcmxV2c9NNhgWJF0pW1T27V+2vaHhUe7jAtJVOLay/Qng6prK8hykpfVd0sI7j6zY1ypl53jShfF5ssHyWVLZrVFkO5UMW6/r3WpEUH6oyPJ3sg9rKbJi+hWVcVl9SH+bUp6qymVUL84fX/J+dWBrciDjMj2c03g/Vy/P+TBSae5TOeaLwGc6l50u0tqYHCOzQ5Flx0o+HUi6mSfVlR9dXH94p/+RZJ/VE2SDdBmybriXbEh+u9zzvKRi/kzJw3675lpyg+368UZf/nqlolmorC9Btux261ywekh3cYp7gfSnv0hl1G3ZfjhZmb+LikIp+w4hW5P3MusI1xPJSrbpjrFSCCaVSuMjZdunyfDF9cvLvzM9hJKWQnZoKVjvJTsmD+h0zDbkuJpGiOZaZCW9T1mfuzcvfiXdrUgl1cin/oa97ljkHE2a/UeWl6YROrkQXVhUpNvkJ+Vl2oZsZd5NWmonkn0qb+jgJhsB55NK5TyyRdhoIBxKWglNuUuavL/fkX7wlUnXxgdJhfNykferpAK8g3RVNRoYXVpn/ZBjKrNax58mK63xjbLZ3+uVsvEksFMfzh1R3sNry7vWlJuJbKz9nKyELyCtvSdJZblDecYbzeb89Sgd+aRyeRTYuVG2S5k4io6xRN26o/v4zJapLE8kI86OIF3jy5GRX6eUcjSzlJVFSOX3TbLuaCiXT9CkB2e2MtV5g4PhR/ryHyEthGFkq3x7ikuB9B3u2ss031EyYfFSAW1WCshHmdWd9FE6ddSS8enfJSvlA0lrp9oa+hLNuZGqiqox6O0sOkzthpJ4kkrneg/pLE26Tk4lo6EuAdYmTeUtyP6oeTudvwbwLE2MXO7hfiaS/Q99NrnpcFXcQLbYlynrq5PK5Ty66aQuFcbtpDW2VakMtiX90lPIyvoN4bilLN1HhxtzW9KFdizp6riEPsxuMJs8Gk4qzR+TfvAPFbmvoyNMfZ2Sj+eQLfYTqGEGCTpa80uW/yMoMw6UfJuPbJE/S7pU+jS6vovrbkYT1ns3585FWlBNjRUiG5s30xEifhBpoRxHegam0oMVRtY5YyvPqaFc9i7rDQW/P/2c4qab659N1nkrlvf4IFKRXUgGWCxX1u8n3YMfIhu/c5Hu1ZPIvtN+uaJnkanumxwMv1JR/ImOcSTfIyvho0uGj+9jgf16qTzmIX3dvyKjRHYl+1mGd6oU3ku6CxoDJpcg/e6n04s5ySov+Pqkomr082xf7qtRgEfSEZ3V1didqmwN3+oCZBjqLeXZHEpWjlfSjcIjW0LL15BP8/fz/CUryz8Gvl9ZfyeVAV106mMirbqvVNbXIweLzXZgLGmtHFWWGxXGe8n+s4voYdxRs3ldec5LV9bPI/3gm5FBJ+eUMjcXWbl9mWwM9LqPYzZlbqtSHkaSVu70yv1vUN6pd/b3eu36kS3339IRgDMX2Ri9jIrV1M37VHWpr0FaPNuX9Y3K+7Rbq2TvJMuFpGVyQFlfhnRbX1DKy3CywXhsubdNKue+s2zvdfh5t/K0O2Nb+KC3Jjte5yVboQeSLZGmWwyVl2tVUkmtT7pIjiUtojXJ1uq1paKqVgofJcOHjyFb0w1lMJJsuZxMk/075bxtyU7bI8jO04apvU2p0D7WzL2U5UOLzJfSEfL7MbLTt98dvQOUvweTFs9X6ehPuQq4qHLM8C7Om0T2R32ZjrDzRj6fTg+DBsmK9hoqCohsAW5Z8/19krQ+fkEqlLmKrJeQbp5HyQiwRn/Ee8kQ6YVrlOE9ZITX+pVtSxS5LiDduFu1uyzUcJ+fIqMrG8EIm5Kj4s+lmxH6Xbzre5MNmeuAbSt58jy9nHmgH/dxAfDXyvqYItsvSLdpY77Cj5PRn6uXPH59zFBtsrQ7U1v8oLchzb9GR3HTI0craWxPmsqNULz1SdPxGNKnP0cl/UYF9RHSRTCmrB9NjiVpDKBclF4MiKIjbHBsKQS/J90/e5f9E2kyLJBsZV5Djj/Yr1QcG5Juw6PIynY+avLLtyhfVyVDutclXYkn0+HjvhE4u3JstQLoPFXIP8iGwtYlzx6kh/4uspP/OFIxbUtaq7+jD1bwbK6xK/CbsvxF0jq5iI7IrIlkX9J25JiNy0h3yLY1XLtRhhcuZfzTpO99P7IxMrVShvtttQ6GX3mvjivvxVRy0ODaZENljR7O7fyuTyEDeTYr6++mn5GBPeTTaqQlUh3L9IdO9zaWDPCYTodyOZhsiDxcZ9l9/brtztQBKDSTyM63OeilYikV2L10hOmNLC/UGLJ1/wXe2AcxD9khvAXZJ/ORkql/Lr9eu0pI98YqpZDeXSqVj5Gt1i6nZ+kmnU1IBVUNINid9M9uVJ7Ror2Vb4DzczvS4muM5RhNWmAnU8x7Ovo/qkplGWadKmQ70gK8vTzLpqc4Kdc8oFQgF1MZCNvHe+oc+rwK2f91IOmSnKdUAFfTqWVJ9hXdS0fDpz8d543K6v1kBbsxGQJ8Ax0zdd9KCwf0tbFcLUhW0IeTLfnGeKhu+8s6veujSpk4jnSJ/YZKhGGLZJ5Y6oOryD6SRuRao89lfWa1No8gGyKNd2BlejmivmnZ2p2hA1RoeuXL7/SCXUdGeh1DuqCeJy2ItegmzJZstfye9Id/lWwdHEWG8/XYyqtcfyWytdGIytqLMtiNbH2fXC043aVTWZ+b7JP5CbOGyO5XCmiPE/O1OR93I92KJ5MRNo3pbxYr+XMCHQNPq0qlp6lCNqAPnd2k3/oN7ra+lLWyPJqOSLnhZOuz4bP/HFmpL95FGrU1BsjGyxfomJR1WTpC5Fcg3Y/j2l0WWlzONi7lZY0mju3qXT+W7HtdpkXyiXTxX0HHkIeNyD7gSWQD8SqyIfI1ylT/5bjzyZk0+hQc0bSM7c7EwfSrVOiNilxkDPrvyJb9wqUC26WHdOYmI3UalcQeZNhz05UQ6cu/lxyb8GdyBPMeZdvBpYLtdsaAThXWpqQvdUHS5XUOaXEtVTmm9rmKas6b95ARL43onf1IV9Z7yvrIripY+jlVyADe3+GkG++hyj0dWfLpS3RqDLTg+g032y9J11vDtdOIvNuWbAVPbPezGoC8GN2sUujiXd+dtPD61eDo4jrzVPJomfL/W8q4GLIhclgpL42w78Z8fsdTolXJUPWraIF7bhZ5252Jg+1HmsO/JDX7F5l19Pp6pEviXU2mNQcZmXEfvXCBkS3D28gJ/HYtymShsm8P0qTdusm0Pk2a5d8r97RBUS7fI/32/ZrCfQDz5SPkOI2j6LA49qFiuXRxTr+mCmnx/VQV/4JkVNuC5Z7+RPZ7rUhaW5fTIvcTHY2p6vQjP2fWGZ2Hl7L//s6y+/f6M+rTu96L9LcvCmJb0mpcgAwY+B4dlvsmZCP4x6WOWJucR/Dr5FCEC8j+4tqiv7qVt90ZMph+ZJ/KNLJ1vDVpnVxSKqg1yBZb0x2kpLm6D8197a46w+5IclDlh5h1KobNqPTp9PSCl+N/WpZPLAXyDNL3Oowcv9LrCeYGOE+2Bj5flncngwt2peMzBbszmxBh+jlVSIvuqfM0P58FLqhs+zDptmtU5C1VgGRj6tIiRyOi6Sbgynbn/1D59eZd78c1bgP+CWxc1lcgI7zuI91wz5bldcjB2leXvB1RlM5RrZRvFlnbnSHt/nV6ydcCTi/Lc5D9G98rmTIHHVFdvfnmdk+V/wKV5Y3JDsDNyY75JyoV6PrkuJlu+2h441iNVcmO/n3IvqKxZDTVr2hyyu92/+j4JEDjA2MfJt1ZH262wqWfU4W08N42K5XFV0hX6WfocNF9lPTdtzRCj7SMHihl/2fAxZV9d1AmUvWvqWdZez7RYVHOX8rEdaTnofppgrVJ6+U75CwTh5DuuAOKcpk04M+i3ZkxGH7l5dqjVMDPUonNJ8c2fLhF152XbBnuSPYBPECGjk4lW9XPkuMZDiGtjYlNprsKs052dzwdLdGjSsXcr9HhA5AnDZ/1nOQArisqyuUg0gLrTch2n6cKqfGeqo2YPcixEo1orsZknlXlstAAyLQn6WZZh3SjNKamWbD8dxsc4t/AlBcygvGrlMANcgzK5WV5w0a9QDZ+VwR+XtaXIBsuZ/XmXalF9nY/vEGQaY2wwgtJ98jdpaU2hYyQuYcWDhok53y6nYyhX6Ns24N0S3yb7Hj+BB1x8V2NAH4nZYAk2ap5gGzlf5Ds9NuX7Oj+Qrm/WqZxb+Ez2Zic823Nsj6M9PHfSkdI5UJ9SLfPU4XUcE/VUdobkz7wp4Ajy7YRpGV1LvCJ7vK6Bjk6f9mzMW/bvRQrrlRkx3U+1r+2lJtNSffWep22/4BsbD1DhrwfQ4aojyvv+HA6Pvg24Nb5MN6iRERIWpe0DvaPiNslLUdaCRuS/Rt/Ib+mdmsL5bhc0kukj3tzUpFdXK4/P/DHiPhmVe7q+ZJEhttuI2k0OS/QhqQVtBnZyXc2GSa9FjlP2mOtup/+Iumj5HfSLwEukLRbRNwL3C7pYWAjSWdFxHO9TTsirq1Z3N5c+38AktYjlcnmkm4FzpX0aET8QNL1ZCjoPeWc6D7F3iFpvoh4OSJeK+V+WdJdcgNpNT8PjCjyTSWnbXmtruubPrMR2en+oKQ9yXf6wYjYWdKp5Fi508kO+oiIL0m6i4wwXBCYHBEzB1rot6xiKSxEZtwmpNXwBNnaH0l+vrdRGajOl7wzEXGtpH2AqZKmR8TFkn5Qdt/T3XmSFidH/V8r6f3lPqZHxPPAmZL+TQYizE2GGF/WyvvoL5I+QvadTIyICyW9AJwl6VNkf9FwcraBXiuVdiFpedJa+ZOk7cmotNMAIuJGSQcC35I0d0ScR06/UbcMiwKfkHQTHZ8Pvp0c97NdWX8/6Yb9O/lV1Z+2utyb7pG0GumJ+A05AHhHsr/kVmBtSaOAf5HTxnyMnLX4RElzRsS+kt4GvBIRf2/LDbTb1Gv3jxznMI1Z51z6HdkSGNCwSjIC6i5gryaPH09Os3EO2fLcjZxe4mOVY/aj8g32wfqj+xkLHijy30Qfv2bXxnsaX+5hIVIpjij5dVWn47Yn3RcLtKLMkb72L5FBAhfTMT/c4eTko2uW9UXo6FtxSHF7yozIvpLdgePKtmUp7msymOVhOmYy/ln5bwT5HEAbOuvfcB/tFmAw/MhW2wuk3/IcaphzqR+ybE+Gmr6NJqagIfuFXqDMlkz6zH8CfLxyzKAe/FiRs6tRzMeTJv2gnhWgi3vZhgztHkNOVvr1sjyCdFN8t9Px3X72uB8yjKDj2yyrk3PW3UzpwynbDy3lbd26r+9fn/KsoSCWI/tWJpEzf+xKeiSeJhvCJ5EWzN+AD5Zz9iYH2LZ9DrdGB/ZbnuKm+DxwYUR8o/RdEG14QJJGRZN+UUkrkIMePwUcH+mrX5t0t5wSERe2UNRakTQ3WQH+OSKek7QbaXFtExH/bK90zSNpLjJy52Qymm8NYBcyKu3bZH/G1cDTEbFrOadWt5OkOclysTo5kn5Vsq9tEmnB3BARV5djDwdujohb6rq+6T2SViRdwRdGxH2lTlqTLEOnkNblSDJAZwUyT7cnp366hszrvSLiwYGXflbe6n0srxMRV5Y+ibMlPR4Rl7VRlqY72yJiGjBN0vNkH83zZJ/Kf8iCOGSIiH8Dv5M0h6R9yWi4XYaYUtmUHDl/Adl38VJELC/pETLU+xBykOc2wI8kjY6IGXU3YCI76Z+mfC+F/DDbHyVdRIYYbyRpeERcHhEn1Hlt02cWIr9Oe66k75MTp44mG4n7k1bKPRHxqKTpZENlSdIa/i0ZxTfgHfVdMUe7BRhMRMQvybEsd7dZlF4TEVeRcwV9jZxj6oAYxNFfPTA3OVXLhyLi/nYL0yyS3kmGrc9DWgn/AV6SNDIiHifde4uS+bNgRGwbETNaIEfD2v4T2al7C7CqpLER8Vdyeo+XgQ1KAIhpA418krSmpI2BRyLiONJK/yP5JcwtyaEHvyIbCVtL2jki/hMRD5F9LXNHxHODRakAdoW92SgVRQymQtYXhlpEUqkkNiErgt+R7q9TgJ1IC2G3iHhE0kpkB+vUVuRRiQp6TdLS5Pc3gmz5fpxUdEeQQQKbkq3fP9ctg2keSduQFsc1ZCjxwcBNEfG/4hr+IBlQ9MmI+KekbUmL9wJyvN2XyEG/gyofrViMqQlJI8iovGXIT93eWxTOkWSAyL4R8aCkuSLivzVfexnyfX5c0tbkWJQbyM77KSV8dV9yYtPVgR0j4q46ZTC9Q9LKpIL4ANlfcj4ZFfoN8kNv/y7H3UKOp7uurE8iJ5r8IXBEsYYHFXaFGdMPGu6MwoJk+OctwFaSlozky+RcYKeWjv1aBx5KGkfOtDBa0gakUplMjuyfKOnK4lL8Ilkh7Wel0l5K39pDZLTXOHKmg+VIN/y5wHskjZA0lgy2eLRxbkT8hBx3dPhgVCpgxWJMn6m66yQdTM7z9SIZtfM+4IAykI2I+CzpsvhvlIG3dclAx0SdT5EDYvek44uZ6wELSPop8HxEnBcR13dSiGYAkTQSOFrSuyPij+TnhW+PiJdIl9g0YEZEvBIR08lQ8EeraUTEjRHxlwEXvkmsWIzpIxWl8lGyEv8WGfb9MXJQ55rAYZIWK8fXPgq6yHAdaaHcA/wyIu4jPxdwQmnR3kiOoZnQWXYzsEhapJSDxtRRkO6vlSWdQgbfHB0R91eCMIbMTBMNrFiM6QeSFiTnYPsQWZk3Zm3YnRwgucwAiPECOdbhBTJoAOAVYHlJO5DTqn8oIn43ALKYbpC0JhlivhM5k/XukiZHxB3kBLH/Aj4bETfD0Fb+7rw3pp+UTvuVgJMjYmNJcwDPkd8+PzMiXh4gOZYm+3KmAveTYasrAKe1c1yWSYpi+QE5I8KnyL6VzYEpEfFE5bghFRHZFbZYjOknEfEK+WW/YZJWJ0OOG9/MGBClUuR4grScDifn/zoA2C4iLnOfSvuQtHKxUh4l8+d64B3k7OWbA9uVmRKAoW2pNLDFYkwNFKvlE+T4kCVI19PDbZJlbfJLoRsP1qihNzOS5iW/n/IrSauSo+ZfIq3HX5HfUJkREbdJOhG4rgzOftNgxWJMTZRQ4iWB/5UR7u2UZYGIeLGdMrxVkbQAOcvC8uRXYnchO+vXJwfN/oecJ27LyjlD3v1Vxa4wY2qihBI/2W6lUnip3QK81ZA0TtKni0K/mgyamBkRT0TEPyPiBnLSyKuBlcpULm2b7LaV2GIxxpgaKLMf3EaOnL+QHJ/yceAvEXFQOWZ4RPynNzOYD0VssRhjTD+RNKwMWNyA/C7KPpGfwv4osIKkb0paA/iepEXfzEoFrFiMMaZflP6RV8v3VF4mv4I6WdLRJVJvT/JroucCPxyKAx57i11hxhjTTyRtR8739Tg5Jcu15PdTzouI48sxYyLir2+2jvqu8Ie+jDGmH0haHziGnPZ+M3LGhX8DnwTOkDQiIr7QCOp4sysVsMVijDH9osxAPBpYhLRadiU/pvY3cjzRcxFxY/skHHjcx2KMMf0gIqaXedjeB1wU+bnwc8mvO94VETe+1WY+sCvMGGPq4T7gI5KGkR92O6QxB9hbwf1VxYrFGGPq4WfkBJPbAydGxK1tlqdtuI/FGGNqpIxpefWtEP3VHe5jMcaYenkN3nruryq2WIwxxtSKLRZjjDG1YsVijDGmVqxYjDHG1IoVizGDCEl7Szq13XIY0x+sWIxpI9VvnRvzZsGKxZg+IukwSR8ryydJuqEsbyLpQkm7SLpP0v2STqic95KkL0q6HdhA0j6S/ijpRmDDynE7lXPvkXTTQN+fMX3FisWYvnMT8J6yPAGYv3z3/t3An4ATgPcDawLrSJpUjp0PuD8i1gP+DHyBVCibAatU0j8G2CIi1iBHcxszJLBiMabv3AWsLWkB4BXgVlLBvAd4Hvh1RMyMiFeBi4D3lvNeA35clterHPcf4AeV9G8BzpW0P2CXmRkyWLEY00ci4r/kh532AX4L/AbYGFgeeGI2p/47Il6rJtVN+gcAnwWWAu6WNLIGsY1pOVYsxvSPm4BDy/9vgAOAu4HbgPdJWqx00O8CdPVNjtuBjSSNLG60nRo7JC0fEbdHxDHAs6SCMWbQ49mNjekfvwGOBm6NiJcl/Rv4TUTMkHQk8CtAwM8i4orOJ5fjPk+60WYAv6fD7fVVSePL+dcD97T8boypAc8VZowxplbsCjPGGFMrVizGGGNqxYrFGGNMrVixGGOMqRUrFmOMMbVixWKMMaZWrFiMMcbUihWLMcaYWvl/9J+NQwq9ryIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_non_hatredhashtag = pd.DataFrame(freq_non_hatred_hashtag, columns=['words', 'frequency'])\n",
    "sns.barplot(x='words', y='frequency', data=df_non_hatredhashtag)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title('non-hatred hashtag frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This includes:\n",
    "\n",
    "\t- Count number of words\n",
    "\t- Count number of characters\n",
    "\t- Average word length\n",
    "\t- Number of stopwords\n",
    "\t- Number of special characters\n",
    "\t- Number of numerics\n",
    "\t- Number of Uppercase words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Count number of words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- One of the most basic requirement in NLP analysis is to count the number of words in each tweet. The idea behind this is that **the negative sentiments contain a lesser amount of words than the positive ones**.\n",
    "\n",
    "- We can accomplish the above task (count the number of words) by using the **split** function in python as follows-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define word count function\n",
    "def num_of_words(df):\n",
    "    df['word_count'] = df['tweet'].apply(lambda x : len(str(x).split(\" \")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>tidy_tweet</th>\n",
       "      <th>tidy_tweet1</th>\n",
       "      <th>hashtag</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "      <td>dysfunctional selfish drags kids dysfunction #run</td>\n",
       "      <td>DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN</td>\n",
       "      <td>run</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks #lyft credit cause offer wheelchair van...</td>\n",
       "      <td>THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...</td>\n",
       "      <td>lyft  disapointed  getthanked</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>majesty</td>\n",
       "      <td>MAJESTY</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model</td>\n",
       "      <td>#MODEL</td>\n",
       "      <td>model</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society #motivation</td>\n",
       "      <td>FACTSGUIDE SOCIETY #MOTIVATION</td>\n",
       "      <td>motivation</td>\n",
       "      <td>8</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49154</th>\n",
       "      <td>49155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>thought factory: left-right polarisation! #tru...</td>\n",
       "      <td>thought factory left right polarisation #trump...</td>\n",
       "      <td>THOUGHT FACTORY LEFT RIGHT POLARISATION #TRUMP...</td>\n",
       "      <td>trump  uselections      leadership  politics ...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49155</th>\n",
       "      <td>49156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>feeling like a mermaid ð #hairflip #neverre...</td>\n",
       "      <td>feeling mermaid #hairflip #neverready #formal ...</td>\n",
       "      <td>FEELING MERMAID #HAIRFLIP #NEVERREADY #FORMAL ...</td>\n",
       "      <td>hairflip  neverready  formal  wedding  gown  ...</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49156</th>\n",
       "      <td>49157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#hillary #campaigned today in #ohio((omg)) &amp;am...</td>\n",
       "      <td>#hillary #campaigned #ohio used words assets l...</td>\n",
       "      <td>#HILLARY #CAMPAIGNED #OHIO USED WORDS ASSETS L...</td>\n",
       "      <td>hillary  campaigned  ohio  omg    clinton  ra...</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49157</th>\n",
       "      <td>49158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>happy, at work conference: right mindset leads...</td>\n",
       "      <td>work conference right mindset leads culture de...</td>\n",
       "      <td>WORK CONFERENCE RIGHT MINDSET LEADS CULTURE DE...</td>\n",
       "      <td>work  mindset</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49158</th>\n",
       "      <td>49159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>my   song \"so glad\" free download!  #shoegaze ...</td>\n",
       "      <td>song glad free download #shoegaze #newmusic #n...</td>\n",
       "      <td>SONG GLAD FREE DOWNLOAD #SHOEGAZE #NEWMUSIC #N...</td>\n",
       "      <td>shoegaze  newmusic  newsong</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49159 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  label                                              tweet  \\\n",
       "0          1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1          2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2          3    0.0                                bihday your majesty   \n",
       "3          4    0.0  #model   i love u take with u all the time in ...   \n",
       "4          5    0.0             factsguide: society now    #motivation   \n",
       "...      ...    ...                                                ...   \n",
       "49154  49155    NaN  thought factory: left-right polarisation! #tru...   \n",
       "49155  49156    NaN  feeling like a mermaid ð #hairflip #neverre...   \n",
       "49156  49157    NaN  #hillary #campaigned today in #ohio((omg)) &am...   \n",
       "49157  49158    NaN  happy, at work conference: right mindset leads...   \n",
       "49158  49159    NaN  my   song \"so glad\" free download!  #shoegaze ...   \n",
       "\n",
       "                                              tidy_tweet  \\\n",
       "0      dysfunctional selfish drags kids dysfunction #run   \n",
       "1      thanks #lyft credit cause offer wheelchair van...   \n",
       "2                                                majesty   \n",
       "3                                                 #model   \n",
       "4                         factsguide society #motivation   \n",
       "...                                                  ...   \n",
       "49154  thought factory left right polarisation #trump...   \n",
       "49155  feeling mermaid #hairflip #neverready #formal ...   \n",
       "49156  #hillary #campaigned #ohio used words assets l...   \n",
       "49157  work conference right mindset leads culture de...   \n",
       "49158  song glad free download #shoegaze #newmusic #n...   \n",
       "\n",
       "                                             tidy_tweet1  \\\n",
       "0      DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN   \n",
       "1      THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...   \n",
       "2                                                MAJESTY   \n",
       "3                                                 #MODEL   \n",
       "4                         FACTSGUIDE SOCIETY #MOTIVATION   \n",
       "...                                                  ...   \n",
       "49154  THOUGHT FACTORY LEFT RIGHT POLARISATION #TRUMP...   \n",
       "49155  FEELING MERMAID #HAIRFLIP #NEVERREADY #FORMAL ...   \n",
       "49156  #HILLARY #CAMPAIGNED #OHIO USED WORDS ASSETS L...   \n",
       "49157  WORK CONFERENCE RIGHT MINDSET LEADS CULTURE DE...   \n",
       "49158  SONG GLAD FREE DOWNLOAD #SHOEGAZE #NEWMUSIC #N...   \n",
       "\n",
       "                                                 hashtag  word_count  \n",
       "0                                                    run          21  \n",
       "1                          lyft  disapointed  getthanked          22  \n",
       "2                                                                  5  \n",
       "3                                                  model          17  \n",
       "4                                             motivation           8  \n",
       "...                                                  ...         ...  \n",
       "49154   trump  uselections      leadership  politics ...          13  \n",
       "49155   hairflip  neverready  formal  wedding  gown  ...          15  \n",
       "49156   hillary  campaigned  ohio  omg    clinton  ra...          20  \n",
       "49157                                      work  mindset          15  \n",
       "49158                        shoegaze  newmusic  newsong          12  \n",
       "\n",
       "[49159 rows x 7 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# call the function\n",
    "num_of_words(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count number of characters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can also calculate the number of characters in every tweet. The intuition is same as above.\n",
    "\n",
    "- This can be accomplised by calculating the length of the tweet as follows -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build the function\n",
    "def num_of_characters(df):\n",
    "    df['char_count'] = df['tweet'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>tidy_tweet</th>\n",
       "      <th>tidy_tweet1</th>\n",
       "      <th>hashtag</th>\n",
       "      <th>word_count</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "      <td>dysfunctional selfish drags kids dysfunction #run</td>\n",
       "      <td>DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN</td>\n",
       "      <td>run</td>\n",
       "      <td>21</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks #lyft credit cause offer wheelchair van...</td>\n",
       "      <td>THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...</td>\n",
       "      <td>lyft  disapointed  getthanked</td>\n",
       "      <td>22</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>majesty</td>\n",
       "      <td>MAJESTY</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model</td>\n",
       "      <td>#MODEL</td>\n",
       "      <td>model</td>\n",
       "      <td>17</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society #motivation</td>\n",
       "      <td>FACTSGUIDE SOCIETY #MOTIVATION</td>\n",
       "      <td>motivation</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
       "\n",
       "                                          tidy_tweet  \\\n",
       "0  dysfunctional selfish drags kids dysfunction #run   \n",
       "1  thanks #lyft credit cause offer wheelchair van...   \n",
       "2                                            majesty   \n",
       "3                                             #model   \n",
       "4                     factsguide society #motivation   \n",
       "\n",
       "                                         tidy_tweet1  \\\n",
       "0  DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN   \n",
       "1  THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...   \n",
       "2                                            MAJESTY   \n",
       "3                                             #MODEL   \n",
       "4                     FACTSGUIDE SOCIETY #MOTIVATION   \n",
       "\n",
       "                          hashtag  word_count  char_count  \n",
       "0                             run          21         102  \n",
       "1   lyft  disapointed  getthanked          22         122  \n",
       "2                                           5          21  \n",
       "3                           model          17          86  \n",
       "4                      motivation           8          39  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# call the function\n",
    "num_of_characters(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that character counts in every tweet has been calculated above.\n",
    "\n",
    "- The above calculation will also include the number of spaces, which we can remove, if required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average word length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is also another feature which is also important is average word length of each tweet. This feature can help us improve our model.\n",
    "\n",
    "- Average is found by taking the sum of the length of all the words and divide it by the total length of the tweet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg_word(sentence):\n",
    "    words = sentence.split()\n",
    "    average = sum(len(word) for word in words)/len(words)\n",
    "    return average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg_word_length(df):\n",
    "    df['avg_word'] = df['tweet'].apply(lambda x: avg_word(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>tidy_tweet</th>\n",
       "      <th>tidy_tweet1</th>\n",
       "      <th>hashtag</th>\n",
       "      <th>word_count</th>\n",
       "      <th>char_count</th>\n",
       "      <th>avg_word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "      <td>dysfunctional selfish drags kids dysfunction #run</td>\n",
       "      <td>DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN</td>\n",
       "      <td>run</td>\n",
       "      <td>21</td>\n",
       "      <td>102</td>\n",
       "      <td>4.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks #lyft credit cause offer wheelchair van...</td>\n",
       "      <td>THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...</td>\n",
       "      <td>lyft  disapointed  getthanked</td>\n",
       "      <td>22</td>\n",
       "      <td>122</td>\n",
       "      <td>5.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>majesty</td>\n",
       "      <td>MAJESTY</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model</td>\n",
       "      <td>#MODEL</td>\n",
       "      <td>model</td>\n",
       "      <td>17</td>\n",
       "      <td>86</td>\n",
       "      <td>4.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society #motivation</td>\n",
       "      <td>FACTSGUIDE SOCIETY #MOTIVATION</td>\n",
       "      <td>motivation</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
       "\n",
       "                                          tidy_tweet  \\\n",
       "0  dysfunctional selfish drags kids dysfunction #run   \n",
       "1  thanks #lyft credit cause offer wheelchair van...   \n",
       "2                                            majesty   \n",
       "3                                             #model   \n",
       "4                     factsguide society #motivation   \n",
       "\n",
       "                                         tidy_tweet1  \\\n",
       "0  DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN   \n",
       "1  THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...   \n",
       "2                                            MAJESTY   \n",
       "3                                             #MODEL   \n",
       "4                     FACTSGUIDE SOCIETY #MOTIVATION   \n",
       "\n",
       "                          hashtag  word_count  char_count  avg_word  \n",
       "0                             run          21         102  4.555556  \n",
       "1   lyft  disapointed  getthanked          22         122  5.315789  \n",
       "2                                           5          21  5.666667  \n",
       "3                           model          17          86  4.928571  \n",
       "4                      motivation           8          39  8.000000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_word_length(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'after',\n",
       " 'again',\n",
       " 'against',\n",
       " 'ain',\n",
       " 'all',\n",
       " 'am',\n",
       " 'an',\n",
       " 'and',\n",
       " 'any',\n",
       " 'are',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'as',\n",
       " 'at',\n",
       " 'be',\n",
       " 'because',\n",
       " 'been',\n",
       " 'before',\n",
       " 'being',\n",
       " 'below',\n",
       " 'between',\n",
       " 'both',\n",
       " 'but',\n",
       " 'by',\n",
       " 'can',\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'd',\n",
       " 'did',\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'do',\n",
       " 'does',\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'doing',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'down',\n",
       " 'during',\n",
       " 'each',\n",
       " 'few',\n",
       " 'for',\n",
       " 'from',\n",
       " 'further',\n",
       " 'had',\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'has',\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'have',\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'having',\n",
       " 'he',\n",
       " 'her',\n",
       " 'here',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'him',\n",
       " 'himself',\n",
       " 'his',\n",
       " 'how',\n",
       " 'i',\n",
       " 'if',\n",
       " 'in',\n",
       " 'into',\n",
       " 'is',\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'just',\n",
       " 'll',\n",
       " 'm',\n",
       " 'ma',\n",
       " 'me',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'more',\n",
       " 'most',\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'my',\n",
       " 'myself',\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'now',\n",
       " 'o',\n",
       " 'of',\n",
       " 'off',\n",
       " 'on',\n",
       " 'once',\n",
       " 'only',\n",
       " 'or',\n",
       " 'other',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'out',\n",
       " 'over',\n",
       " 'own',\n",
       " 're',\n",
       " 's',\n",
       " 'same',\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'so',\n",
       " 'some',\n",
       " 'such',\n",
       " 't',\n",
       " 'than',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'the',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'them',\n",
       " 'themselves',\n",
       " 'then',\n",
       " 'there',\n",
       " 'these',\n",
       " 'they',\n",
       " 'this',\n",
       " 'those',\n",
       " 'through',\n",
       " 'to',\n",
       " 'too',\n",
       " 'under',\n",
       " 'until',\n",
       " 'up',\n",
       " 've',\n",
       " 'very',\n",
       " 'was',\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'we',\n",
       " 'were',\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'what',\n",
       " 'when',\n",
       " 'where',\n",
       " 'which',\n",
       " 'while',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " 'y',\n",
       " 'you',\n",
       " \"you'd\",\n",
       " \"you'll\",\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves'}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop = stopwords.words('english')\n",
    "set(stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build the function\n",
    "def stop_words(df):\n",
    "    df['stopwords'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x in stop]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>tidy_tweet</th>\n",
       "      <th>tidy_tweet1</th>\n",
       "      <th>hashtag</th>\n",
       "      <th>word_count</th>\n",
       "      <th>char_count</th>\n",
       "      <th>avg_word</th>\n",
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       "      <td>dysfunctional selfish drags kids dysfunction #run</td>\n",
       "      <td>DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN</td>\n",
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       "      <td>21</td>\n",
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       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks #lyft credit cause offer wheelchair van...</td>\n",
       "      <td>THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...</td>\n",
       "      <td>lyft  disapointed  getthanked</td>\n",
       "      <td>22</td>\n",
       "      <td>122</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
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       "      <td>majesty</td>\n",
       "      <td>MAJESTY</td>\n",
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       "      <td>5.666667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model</td>\n",
       "      <td>#MODEL</td>\n",
       "      <td>model</td>\n",
       "      <td>17</td>\n",
       "      <td>86</td>\n",
       "      <td>4.928571</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society #motivation</td>\n",
       "      <td>FACTSGUIDE SOCIETY #MOTIVATION</td>\n",
       "      <td>motivation</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
       "\n",
       "                                          tidy_tweet  \\\n",
       "0  dysfunctional selfish drags kids dysfunction #run   \n",
       "1  thanks #lyft credit cause offer wheelchair van...   \n",
       "2                                            majesty   \n",
       "3                                             #model   \n",
       "4                     factsguide society #motivation   \n",
       "\n",
       "                                         tidy_tweet1  \\\n",
       "0  DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN   \n",
       "1  THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...   \n",
       "2                                            MAJESTY   \n",
       "3                                             #MODEL   \n",
       "4                     FACTSGUIDE SOCIETY #MOTIVATION   \n",
       "\n",
       "                          hashtag  word_count  char_count  avg_word  stopwords  \n",
       "0                             run          21         102  4.555556         10  \n",
       "1   lyft  disapointed  getthanked          22         122  5.315789          5  \n",
       "2                                           5          21  5.666667          1  \n",
       "3                           model          17          86  4.928571          5  \n",
       "4                      motivation           8          39  8.000000          1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call the function\n",
    "stop_words(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Number of hashtags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We want to calculate the number of hashtags in it. It also helps in extracting extra information from our text data.\n",
    "\n",
    "- Here, we make use of the `starts with` function because hashtags always appear at the beginning of a word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hash_tags(df):\n",
    "    df['hashtags'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>tidy_tweet</th>\n",
       "      <th>tidy_tweet1</th>\n",
       "      <th>hashtag</th>\n",
       "      <th>word_count</th>\n",
       "      <th>char_count</th>\n",
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       "      <th>stopwords</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>majesty</td>\n",
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       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>#model</td>\n",
       "      <td>#MODEL</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society #motivation</td>\n",
       "      <td>FACTSGUIDE SOCIETY #MOTIVATION</td>\n",
       "      <td>motivation</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
       "\n",
       "                                          tidy_tweet  \\\n",
       "0  dysfunctional selfish drags kids dysfunction #run   \n",
       "1  thanks #lyft credit cause offer wheelchair van...   \n",
       "2                                            majesty   \n",
       "3                                             #model   \n",
       "4                     factsguide society #motivation   \n",
       "\n",
       "                                         tidy_tweet1  \\\n",
       "0  DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN   \n",
       "1  THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...   \n",
       "2                                            MAJESTY   \n",
       "3                                             #MODEL   \n",
       "4                     FACTSGUIDE SOCIETY #MOTIVATION   \n",
       "\n",
       "                          hashtag  word_count  char_count  avg_word  \\\n",
       "0                             run          21         102  4.555556   \n",
       "1   lyft  disapointed  getthanked          22         122  5.315789   \n",
       "2                                           5          21  5.666667   \n",
       "3                           model          17          86  4.928571   \n",
       "4                      motivation           8          39  8.000000   \n",
       "\n",
       "   stopwords  hashtags  \n",
       "0         10         1  \n",
       "1          5         3  \n",
       "2          1         0  \n",
       "3          5         1  \n",
       "4          1         1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hash_tags(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 Number of numerics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can also find the number of numerics which are present in the tweets. \n",
    "- It is a useful feature that should be run while doing similar exercises. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def num_numerics(df):\n",
    "    df['numerics'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society #motivation</td>\n",
       "      <td>FACTSGUIDE SOCIETY #MOTIVATION</td>\n",
       "      <td>motivation</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
       "\n",
       "                                          tidy_tweet  \\\n",
       "0  dysfunctional selfish drags kids dysfunction #run   \n",
       "1  thanks #lyft credit cause offer wheelchair van...   \n",
       "2                                            majesty   \n",
       "3                                             #model   \n",
       "4                     factsguide society #motivation   \n",
       "\n",
       "                                         tidy_tweet1  \\\n",
       "0  DYSFUNCTIONAL SELFISH DRAGS KIDS DYSFUNCTION #RUN   \n",
       "1  THANKS #LYFT CREDIT CAUSE OFFER WHEELCHAIR VAN...   \n",
       "2                                            MAJESTY   \n",
       "3                                             #MODEL   \n",
       "4                     FACTSGUIDE SOCIETY #MOTIVATION   \n",
       "\n",
       "                          hashtag  word_count  char_count  avg_word  \\\n",
       "0                             run          21         102  4.555556   \n",
       "1   lyft  disapointed  getthanked          22         122  5.315789   \n",
       "2                                           5          21  5.666667   \n",
       "3                           model          17          86  4.928571   \n",
       "4                      motivation           8          39  8.000000   \n",
       "\n",
       "   stopwords  hashtags  numerics  \n",
       "0         10         1         0  \n",
       "1          5         3         0  \n",
       "2          1         0         0  \n",
       "3          5         1         0  \n",
       "4          1         1         0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_numerics(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Number of Uppercase words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Sometimes, it is also necessary  to identify UPPERCASE words in a sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def num_uppercase(df):\n",
    "    df['upper_case'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x.isupper()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
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       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
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       "                                          tidy_tweet  \\\n",
       "0  dysfunctional selfish drags kids dysfunction #run   \n",
       "1  thanks #lyft credit cause offer wheelchair van...   \n",
       "2                                            majesty   \n",
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       "4                     factsguide society #motivation   \n",
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       "                          hashtag  word_count  char_count  avg_word  \\\n",
       "0                             run          21         102  4.555556   \n",
       "1   lyft  disapointed  getthanked          22         122  5.315789   \n",
       "2                                           5          21  5.666667   \n",
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       "4                      motivation           8          39  8.000000   \n",
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       "   stopwords  hashtags  numerics  upper_case  \n",
       "0         10         1         0           0  \n",
       "1          5         3         0           0  \n",
       "2          1         0         0           0  \n",
       "3          5         1         0           0  \n",
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   "source": [
    "num_uppercase(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "   id  label                                              tweet  \\\n",
       "0   1    0.0   @user when a father is dysfunctional and is s...   \n",
       "1   2    0.0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3    0.0                                bihday your majesty   \n",
       "3   4    0.0  #model   i love u take with u all the time in ...   \n",
       "4   5    0.0             factsguide: society now    #motivation   \n",
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       "0  dysfunctional selfish drags kids dysfunction #run   \n",
       "1  thanks #lyft credit cause offer wheelchair van...   \n",
       "2                                            majesty   \n",
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       "0                             run          21         102  4.555556   \n",
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"C:/Users/lenovo/Desktop/hate_speech_detection/data_analyzed.csv\")"
   ]
  }
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