ANALYSIS OF STEVE JOBS SPEECH.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEVE JOBS SPEECH ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "steve=\"\"\"I reached the pinnacle of success in the business world. In others’ eyes, my life is the epitome of success. However, aside from work, I have little joy. In the end, my wealth is only a fact of life that I am accustomed to. At this moment, lying on my bed and recalling my life, I realize that all the recognition and wealth that I took so much pride in have paled and become meaningless in the face of my death.\n",
    "\n",
    "You can employ someone to drive the car for you, make money for you, but you cannot have someone bear your sickness for you. Material things lost can be found or replaced. But there is one thing that can never be found when lost—Life. Whichever stage in life you’re in right now, with time, you will face the day when the curtain falls.\n",
    "\n",
    "Treasure love for your family, love for your spouse, love for your friends. Treat yourself well and cherish others. As we grow older, and hopefully wiser, we realize that a $3000 or a $30 watch both tell the same time. You will realize that your true inner happiness does not come from the material things of this world. Whether you fly first class or economy, if the plane goes down—you go down with it.\n",
    "\n",
    "Therefore, I hope you realize, when you have mates, buddies and old friends, brothers and sisters, who you chat with, laugh with, talk with, sing with, talk about north-south-east-west or heaven and earth, that is true happiness. Don’t educate your children to be rich. Educate them to be happy. So when they grow up they will know the value of things and not the price.\n",
    "\n",
    "Eat your food as your medicine, otherwise, you have to eat medicine as your food.\n",
    "\n",
    "The One who loves you will never leave you for another because, even if there are 100 reasons to give up, he or she will find a reason to hold on. There is a big difference between a human being and being human. Only a few really understand it. You are loved when you are born. You will be loved when you die. In between, you have to manage.\n",
    "\n",
    "The six best doctors in the world are sunlight, rest, exercise, diet, self-confidence, and friends. Maintain them in all stages and enjoy a healthy life.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I reached the pinnacle of success in the business world. In others’ eyes, my life is the epitome of success. However, aside from work, I have little joy. In the end, my wealth is only a fact of life that I am accustomed to. At this moment, lying on my bed and recalling my life, I realize that all the recognition and wealth that I took so much pride in have paled and become meaningless in the face of my death.\\n\\nYou can employ someone to drive the car for you, make money for you, but you cannot have someone bear your sickness for you. Material things lost can be found or replaced. But there is one thing that can never be found when lost—Life. Whichever stage in life you’re in right now, with time, you will face the day when the curtain falls.\\n\\nTreasure love for your family, love for your spouse, love for your friends. Treat yourself well and cherish others. As we grow older, and hopefully wiser, we realize that a $3000 or a $30 watch both tell the same time. You will realize that your true inner happiness does not come from the material things of this world. Whether you fly first class or economy, if the plane goes down—you go down with it.\\n\\nTherefore, I hope you realize, when you have mates, buddies and old friends, brothers and sisters, who you chat with, laugh with, talk with, sing with, talk about north-south-east-west or heaven and earth, that is true happiness. Don’t educate your children to be rich. Educate them to be happy. So when they grow up they will know the value of things and not the price.\\n\\nEat your food as your medicine, otherwise, you have to eat medicine as your food.\\n\\nThe One who loves you will never leave you for another because, even if there are 100 reasons to give up, he or she will find a reason to hold on. There is a big difference between a human being and being human. Only a few really understand it. You are loved when you are born. You will be loved when you die. In between, you have to manage.\\n\\nThe six best doctors in the world are sunlight, rest, exercise, diet, self-confidence, and friends. Maintain them in all stages and enjoy a healthy life.'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from wordcloud import WordCloud, STOPWORDS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_process(text):\n",
    "    text = text.lower() # step 1\n",
    "    text = re.sub('([^a-zA-Z])', ' ', text) # step 2,3\n",
    "    wt = word_tokenize(text) # step 4\n",
    "    sw = set(stopwords.words('english')) # step 5\n",
    "    root1 = PorterStemmer() # step 6\n",
    "       \n",
    "    text_clean = [root1.stem(word) for word in wt if word not in sw]\n",
    "    \n",
    "    \n",
    "    return text_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "steve_cleaned=text_process(steve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reach',\n",
       " 'pinnacl',\n",
       " 'success',\n",
       " 'busi',\n",
       " 'world',\n",
       " 'other',\n",
       " 'eye',\n",
       " 'life',\n",
       " 'epitom',\n",
       " 'success',\n",
       " 'howev',\n",
       " 'asid',\n",
       " 'work',\n",
       " 'littl',\n",
       " 'joy',\n",
       " 'end',\n",
       " 'wealth',\n",
       " 'fact',\n",
       " 'life',\n",
       " 'accustom',\n",
       " 'moment',\n",
       " 'lie',\n",
       " 'bed',\n",
       " 'recal',\n",
       " 'life',\n",
       " 'realiz',\n",
       " 'recognit',\n",
       " 'wealth',\n",
       " 'took',\n",
       " 'much',\n",
       " 'pride',\n",
       " 'pale',\n",
       " 'becom',\n",
       " 'meaningless',\n",
       " 'face',\n",
       " 'death',\n",
       " 'employ',\n",
       " 'someon',\n",
       " 'drive',\n",
       " 'car',\n",
       " 'make',\n",
       " 'money',\n",
       " 'someon',\n",
       " 'bear',\n",
       " 'sick',\n",
       " 'materi',\n",
       " 'thing',\n",
       " 'lost',\n",
       " 'found',\n",
       " 'replac',\n",
       " 'one',\n",
       " 'thing',\n",
       " 'never',\n",
       " 'found',\n",
       " 'lost',\n",
       " 'life',\n",
       " 'whichev',\n",
       " 'stage',\n",
       " 'life',\n",
       " 'right',\n",
       " 'time',\n",
       " 'face',\n",
       " 'day',\n",
       " 'curtain',\n",
       " 'fall',\n",
       " 'treasur',\n",
       " 'love',\n",
       " 'famili',\n",
       " 'love',\n",
       " 'spous',\n",
       " 'love',\n",
       " 'friend',\n",
       " 'treat',\n",
       " 'well',\n",
       " 'cherish',\n",
       " 'other',\n",
       " 'grow',\n",
       " 'older',\n",
       " 'hope',\n",
       " 'wiser',\n",
       " 'realiz',\n",
       " 'watch',\n",
       " 'tell',\n",
       " 'time',\n",
       " 'realiz',\n",
       " 'true',\n",
       " 'inner',\n",
       " 'happi',\n",
       " 'come',\n",
       " 'materi',\n",
       " 'thing',\n",
       " 'world',\n",
       " 'whether',\n",
       " 'fli',\n",
       " 'first',\n",
       " 'class',\n",
       " 'economi',\n",
       " 'plane',\n",
       " 'goe',\n",
       " 'go',\n",
       " 'therefor',\n",
       " 'hope',\n",
       " 'realiz',\n",
       " 'mate',\n",
       " 'buddi',\n",
       " 'old',\n",
       " 'friend',\n",
       " 'brother',\n",
       " 'sister',\n",
       " 'chat',\n",
       " 'laugh',\n",
       " 'talk',\n",
       " 'sing',\n",
       " 'talk',\n",
       " 'north',\n",
       " 'south',\n",
       " 'east',\n",
       " 'west',\n",
       " 'heaven',\n",
       " 'earth',\n",
       " 'true',\n",
       " 'happi',\n",
       " 'educ',\n",
       " 'children',\n",
       " 'rich',\n",
       " 'educ',\n",
       " 'happi',\n",
       " 'grow',\n",
       " 'know',\n",
       " 'valu',\n",
       " 'thing',\n",
       " 'price',\n",
       " 'eat',\n",
       " 'food',\n",
       " 'medicin',\n",
       " 'otherwis',\n",
       " 'eat',\n",
       " 'medicin',\n",
       " 'food',\n",
       " 'one',\n",
       " 'love',\n",
       " 'never',\n",
       " 'leav',\n",
       " 'anoth',\n",
       " 'even',\n",
       " 'reason',\n",
       " 'give',\n",
       " 'find',\n",
       " 'reason',\n",
       " 'hold',\n",
       " 'big',\n",
       " 'differ',\n",
       " 'human',\n",
       " 'human',\n",
       " 'realli',\n",
       " 'understand',\n",
       " 'love',\n",
       " 'born',\n",
       " 'love',\n",
       " 'die',\n",
       " 'manag',\n",
       " 'six',\n",
       " 'best',\n",
       " 'doctor',\n",
       " 'world',\n",
       " 'sunlight',\n",
       " 'rest',\n",
       " 'exercis',\n",
       " 'diet',\n",
       " 'self',\n",
       " 'confid',\n",
       " 'friend',\n",
       " 'maintain',\n",
       " 'stage',\n",
       " 'enjoy',\n",
       " 'healthi',\n",
       " 'life']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steve_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\subha\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent=SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0258, 0.0, 0.5719, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5719, 0.0, 0.0, 0.0, 0.0, 0.5859, 0.0, 0.4939, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4939, 0.0, 0.0, 0.34, 0.0, 0.0, -0.4404, 0.0, -0.5994, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5106, 0.0, 0.0, -0.3182, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.3182, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6369, 0.0, 0.6369, 0.0, 0.6369, 0.4939, 0.4019, 0.2732, 0.3818, 0.0, 0.0, 0.0, 0.4404, 0.296, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4215, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4404, 0.0, 0.0, 0.0, 0.0, 0.4939, 0.0, 0.0, 0.0, 0.5574, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5106, 0.0, 0.4215, 0.0, 0.0, 0.0, 0.5574, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6369, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6369, 0.0, 0.6369, -0.5994, 0.0, 0.0, 0.6369, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4939, 0.0, 0.0, 0.4939, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "sentiment_scores=[sent.polarity_scores(word)[\"compound\"]for word in steve_cleaned]\n",
    "print(sentiment_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_words=[word for i,word  in enumerate(steve_cleaned) if sentiment_scores[i]>0.1]\n",
    "negative_words=[word for i,word in enumerate(steve_cleaned) if sentiment_scores[i]<-0.1]\n",
    "neutral_words=[word for i,word in enumerate(steve_cleaned) if sentiment_scores[i]>=-0.1 and \n",
    "               sentiment_scores[i]<=0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive words \n",
      " ['success', 'success', 'joy', 'wealth', 'wealth', 'pride', 'love', 'love', 'love', 'friend', 'treat', 'well', 'cherish', 'hope', 'wiser', 'true', 'hope', 'friend', 'laugh', 'heaven', 'true', 'rich', 'love', 'love', 'love', 'best', 'friend', 'enjoy']\n",
      "########################################\n",
      "negative words \n",
      " ['meaningless', 'death', 'sick', 'lost', 'lost', 'die']\n",
      "########################################\n",
      "neutral words \n",
      " ['reach', 'pinnacl', 'busi', 'world', 'other', 'eye', 'life', 'epitom', 'howev', 'asid', 'work', 'littl', 'end', 'fact', 'life', 'accustom', 'moment', 'lie', 'bed', 'recal', 'life', 'realiz', 'recognit', 'took', 'much', 'pale', 'becom', 'face', 'employ', 'someon', 'drive', 'car', 'make', 'money', 'someon', 'bear', 'materi', 'thing', 'found', 'replac', 'one', 'thing', 'never', 'found', 'life', 'whichev', 'stage', 'life', 'right', 'time', 'face', 'day', 'curtain', 'fall', 'treasur', 'famili', 'spous', 'other', 'grow', 'older', 'realiz', 'watch', 'tell', 'time', 'realiz', 'inner', 'happi', 'come', 'materi', 'thing', 'world', 'whether', 'fli', 'first', 'class', 'economi', 'plane', 'goe', 'go', 'therefor', 'realiz', 'mate', 'buddi', 'old', 'brother', 'sister', 'chat', 'talk', 'sing', 'talk', 'north', 'south', 'east', 'west', 'earth', 'happi', 'educ', 'children', 'educ', 'happi', 'grow', 'know', 'valu', 'thing', 'price', 'eat', 'food', 'medicin', 'otherwis', 'eat', 'medicin', 'food', 'one', 'never', 'leav', 'anoth', 'even', 'reason', 'give', 'find', 'reason', 'hold', 'big', 'differ', 'human', 'human', 'realli', 'understand', 'born', 'manag', 'six', 'doctor', 'world', 'sunlight', 'rest', 'exercis', 'diet', 'self', 'confid', 'maintain', 'stage', 'healthi', 'life']\n"
     ]
    }
   ],
   "source": [
    "print(\"positive words \\n\", positive_words)\n",
    "print(\"########\"*5)\n",
    "print(\"negative words \\n\", negative_words)\n",
    "print(\"########\"*5)\n",
    "print(\"neutral words \\n\", neutral_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_word_freq=nltk.FreqDist(positive_words)\n",
    "negative_word_freq=nltk.FreqDist(negative_words)\n",
    "neutral_word_freq=nltk.FreqDist(neutral_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 17 samples and 28 outcomes>\n"
     ]
    }
   ],
   "source": [
    "print(positive_word_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 5 samples and 6 outcomes>\n"
     ]
    }
   ],
   "source": [
    "print(negative_word_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 111 samples and 143 outcomes>\n"
     ]
    }
   ],
   "source": [
    "print(neutral_word_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_positive=pd.DataFrame(positive_word_freq.most_common(25),columns=[\"Word\",\"Frequency\"])\n",
    "df_negative=pd.DataFrame(negative_word_freq.most_common(25),columns=[\"Word\",\"Frequency\"])\n",
    "df_neutral=pd.DataFrame(neutral_word_freq.most_common(25),columns=[\"Word\",\"Frequency\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>love</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>friend</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>success</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wealth</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hope</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>true</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>joy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pride</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>treat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>well</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cherish</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>wiser</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>laugh</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>heaven</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>rich</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>best</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>enjoy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Word  Frequency\n",
       "0      love          6\n",
       "1    friend          3\n",
       "2   success          2\n",
       "3    wealth          2\n",
       "4      hope          2\n",
       "5      true          2\n",
       "6       joy          1\n",
       "7     pride          1\n",
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           "bgcolor": "#E5ECF6",
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          },
          "scene": {
           "xaxis": {
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            "gridcolor": "white",
            "gridwidth": 2,
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           "yaxis": {
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            "gridwidth": 2,
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           },
           "zaxis": {
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "top neutral words"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
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         "title": {
          "text": "Word"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Frequency"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive_figure=px.bar(df_positive,x=\"Word\",y=\"Frequency\", title=\"top +ve words\")\n",
    "negative_figure=px.bar(df_negative,x=\"Word\",y=\"Frequency\", title=\"top -ve words\")\n",
    "neutral_figure=px.bar(df_neutral,x=\"Word\",y=\"Frequency\", title=\"top neutral words\")\n",
    "\n",
    "positive_figure.show()\n",
    "negative_figure.show()\n",
    "neutral_figure.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordcloud_positive=WordCloud().generate_from_frequencies(positive_word_freq)\n",
    "wordcloud_negative=WordCloud().generate_from_frequencies(negative_word_freq)\n",
    "wordcloud_neutral=WordCloud().generate_from_frequencies(neutral_word_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.imshow(wordcloud_positive)\n",
    "plt.title(\"Positive words\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TT4ZWRUUcw5196qtD7v07fu7mazLPHkVzORZiX7PzWOalpkh/TD78hJB7OjtCfu6mq4eyO8OmtmlayFW1dSHPXPbykEdz3YD+yL7OrrZdIW+454ah7A4Mi/6cH/JybkgpvtaxfG5oOvS4kOef8+ay7auzZXvIbVtifcLedf2qauJxmJ3Pftz4SaXtHNAn15l/1Nd1Zkqj/FqzD/XT54Z80AXvCzn72YGUtv7hnswjY+NYSKnv48Gx8PzG8vEw1rljBAAAAAAAyA0DIwAAAAAAQG4YGAEAAAAAAHJDjZFh1jB7Uch1U2f1uf2u554KecvDd4bcuat5n+3PelGcY76iqn+//sqaWN+gKTO3//TjTt/nc3u64rz/mx+4PeSWNctj3zJzfU7N1AUZTP2SqUefHPJIrjEy46SXhVzKuV57OttDbn7qkZBbMsda587tIfeeJzqllKpq64vL2doYExccFvPiWI8m+/vur6nHnlJczvZz4+9uHFTbw2nTffHYnLT4yIE3lqlf0rphdci7Nzwb8+a1IXdlzi1dba3F5crMuaQ6My94w+yFITcdenzcvn78vnrdb1OPiX/f25+4P+TsOXTsqAhp9qmvCXnK0S8aVOsdzVuLy81P/T6sa10fj6Wu1p0hF3q6Q65umBhyw+zFIU8+PHN8ZLYfjOw5tXnloyFn/w5Gi+xxvf7uX4RcXRf/xqoyf3N9rd/fc7N1AhiJynd+6H1uSGlw5wfnhtKobZoe8ryXla6mSOv6Z0Jee+u1fa4fjPFzDwp5zunnh5yt09gvmc9EbVtjLZSO7ZtDbt+xJa7fEdd3bI/rWzeU7ucA5TJarzNT6vtas5zXmSmNnWvNiqqqkOef++chl7KOxO6Na0LOvse2Z+pRdWY/K3R3hdz7e6nquoawrnZK/D6tftrskMfPXRJyVeb5+1Po6Skub33k3n49dyQbLcdDX8dCSkN7PPQ+FlIaW8dD3rhjBAAAAAAAyA0DIwAAAAAAQG4YGAEAAAAAAHJDjZFhVj99bp/rs3M1P/Pzb4ZcKMR57bJ2Pfd0cblzR5wHesEr3nogXdyn2S9+ZcjZ+f0K3XvmjX76mi+Hda3rVvVrXzufeSLkJa//65CzcwP2JTsn6biJk0Pu3LmtX30rpYaZC0Ke+aJzS9b21j/EOQ/X3/mzkLt27yrZvrK2PHRHyLVTZoa84Jw4h2X9zPkD3tfMk88LedfapzN55YDbHmq9/35T2nvu7oZZe2p3tKxZEdZte+y3Ie9c+VjIXbtbStHFA5Kt87H+jnjszT79z0KeekysKTQYMzPzJz/9o7FaYyTqb82Ano44F/S6u34ecqhnlZmbfbB2rHg45A13XxfynDPOLy4PtlZKdp7p6cefFfLq6741qPaHS9vmdX3mcjrqvf9fyKWcg5jy6M/fUb/ODSmV9PzQn3NDSoM7P4ylc8OMZfF9r7J63IDbyn62WHntf4acneu7lLKfgZ66+oshH/S694acrdvYp4pYdydbK6Xl2Vj7EMaCcl5nphSvNYfyOjOleK1ZzuvMlPq+1hxN15mNS44JuW7q7H1suX/ZmjDP3nBVyM1P/2HAbZdc5vzfMCMeD40HHxty02EvCLm1V82xbN200czx8Ed9HQ99HQspja3jIW/cMQIAAAAAAOSGgREAAAAAACA3DIwAAAAAAAC5ocbICFPo6Q75uVuuiev3U1OkL9uXPxjyjC1xDuL+ziOYrSmStfmh24vL/a0psrc4Z/XmTM2K/tQYyWqYFedb3TGENUYqquKf4Lxz3hzXVwx87HLD3dfH/JtfDritUmvfuiHkp/7nP0Je8vr3hVw/Y94Bt11RGX9m2foVK7732QNua6R57qYfhtzT1Vlcbt+2cai7M2C9+53S3q+rclxNyJMPP3HA+xo//+DYdua8lZ0/Py96ujpCXvnjOG/8cM6RnO3bmht/UFyuqmsI67LzAPdX9vlVdeND7m4r75zYMNKM1nNDSqU9P4ymc0N1fexb02HHD7it7HXIs7/877i+jDVF9if7+8/27bC3/kN8Qmbe8L7MPu01IS//7qf71zkYgcp5nZnSyL3WLOd1Zkp9X2uOpuvMxoOXlqyttbf+KOQRVUMiK1MLrXXD6j7zujtjbbWq2r6/AxutHA9/1NfxkJdjIY/cMQIAAAAAAOSGgREAAAAAACA3DIwAAAAAAAC5ocbICLPruadD7mzZUbZ9NT/9aMj9rTGyP1sfuaek7fXWuu6ZkrVVM2lqydrqr8lHvDDkuikzB9XezlWPFZdHyjyvB6KnM9Z5yM4bfehFH4hP6Me80Q0zYw2ZCfNizYmWNSsOuK3htnvTc8PdhSGx7vafhtx0yHEhZ+dM7kt2/uTxsxeHvPOZx/vXuTFi7a3XhjycdQP6I9vvSUuODrm/82Vn54keP3thyM0r4/skjHWj9dyQUmnPD6Pp3DA+87lmMHUDdmZeV+cQ1t3rr/btm0Le+eyTIU9ccNgBt1U/fW7MmZoDuzeu6WfvYPiV8zozpdFzrVnO68yU4rXmaLrOrJ08fZAt7KnNsP2JBwbZ1kgWa1B0t7cNUz/Ka3DHQ/wZjd3jIR/HQh65YwQAAAAAAMgNAyMAAAAAAEBuGBgBAAAAAAByQ42REaaUtTP2Z/fGZ0vaXmfL9pDbt20saft97aunqzPkyupxB9xWzcSmEvRoYKYe++KStrfuzp+XtL3h0rZlXcg7Vz8R8sSFhw+47SmZn/lInvs1r7pad4bc8uzykCcuOmLAbdc0TYsPDN0pd1i1bV4b8tY/3DtMPRmc7Ll/r2OjH3PKP5/6WbEm0UiqIwDl0vv8MFrPDSmV9/wwks8N2dpZg9Hy3FMla2uoZes0Dub3PX7ukpDVGGE0cp35/Fxn/lFVbcOgnl/o7u613DXY7jDMBnM89D4W/pgdD4wu7hgBAAAAAAByw8AIAAAAAACQG6bSGmHat5dv+qmszp3bS9re7k1r979RmXTvbgm5cuLkA35uZU1dqbuzTw2zFoZcP33uoNprXbcq5Ox0OWPFjhUPhzyYW5wnzD14sN1hiLVmpv0bzFRa1XWDu218tNprepxCYXg6UmLZ6ScHO5VWzYQDf++AsSKcH8bIuSGl0p4fRvK5oXr8pJK11bF9c8naGmod2zeVrK26KTNK1hYMpd7Xmq4zD0xerzO7O3aHPC419ev5FVV7vkqsmzY7rGvbvC67OSPcYI6H3sdCSo4HRh93jAAAAAAAALlhYAQAAAAAAMgNAyMAAAAAAEBuqDEywnS27BiyfWXnERyszuatJW2vP3q6Ogf83MrqcSXsSd8GUxvh+TSveqyk7Y1Uuzc8u/+NDlB1w4SQa6fMDLl964aS7YvS6G7dVbK2KmvqS9bWaLJj+UPD3YWy6Ny5raTtVdUOXc0pGCmcH/ZvJJ8bSlk7q7u9tNcGQ6m7o71kbVXW5vOzAqNfKa81XWf232i6zszWh62bOnsfW+7f3DNfF/LTP/pKyIXurgG3zdBwPJBn7hgBAAAAAAByw8AIAAAAAACQGwZGAAAAAACA3FBjZITp2l26ufT3p6ertHP7dbbuLGl7/TGYeQorqobuz6Bh9sKStlfKOVFHso6d28vWdu3kGSGPpLlf+aOe7oHXEMqqqKwoWVsjWeeu5j7zWFHq+fDNK08eOD/034g+N1SU7n1tKD8Tl1plCfteGETtQhhOpbzWdJ05eCP5OrN5xcMhTz78hAG3NX7ukpAPedOlIT93yzUh73ruqQHvi/JwPJBn7hgBAAAAAAByw8AIAAAAAACQGwZGAAAAAACA3Bi9E8mOUT0dbUO3s0KhpM31tA9h30uphHMzP0/jITXMXFDS1jt2bClpeyNVT0dp6wj0Vl3XULa2Ybi0b1k/3F0YEoUSv49VlPX9AEYG54f+G8nnhq4S1virbphYsraGWvX40vW9q7WlZG1BeZXvWtN15uCN5OvMHU8/EnLb5rUh102bM+C266bNDnnJ698XcramxKYHbgt559N/CLlQ6BlwXzgwo+V4cCxQDu4YAQAAAAAAcsPACAAAAAAAkBslHxj56Ec/mioqKsK/ww8/vLi+ra0tve9970tTp05NEyZMSBdccEHasGFDqbsBAAAAAACwl7LUGDnqqKPSr3/96z07qd6zm0svvTT9/Oc/T1dffXVqbGxMF198cXrd616X7rzzznJ0ZdTp6eoc7i4MWE9313B3YcSpbpgQclVtfUnbP+xtl5e0vTyqqhs/3F0YFjUTJ4dcN2NeyPWZeUTHTZgUclX9nmO7OvMzrKqtC7mialzIleMyuTrmiuq+17N/nS3bh7sLwAjl/DC27N4U5wFvOuz4AbfVMGthyNufuH/AbQ21bN8Ho23zupK1BeVUzmtN15mDN6KvMzN1uFbfcFXIS95wSchVNfH6bjDGz13SZ+7cuS3krY/+NuRtj/6muNzRvLVk/cq1fhwPpTwWUurf8dDXsZCS44GBKcvASHV1dZo1a9Zej+/YsSN97WtfS9/97nfTS17ykpRSSl//+tfTEUccke655570ohe9qBzdAQAAAAAASCmVqcbI8uXL05w5c9JBBx2ULrroorR69eqUUkr33Xdf6uzsTGeffXZx28MPPzwtWLAg3X333ftsr729PTU3N4d/AAAAAAAA/VXygZFly5alb3zjG+n6669PX/rSl9LKlSvTaaedlnbu3JnWr1+fampqUlNTU3jOzJkz0/r16/fZ5hVXXJEaGxuL/+bPn1/qbgMAAAAAADlQ8qm0zjvvvOLysccem5YtW5YWLlyYfvCDH6T6+oHNeXn55Zenyy67rJibm5vH7OBIoad7uLswYIUeNUaySl1ThNLL1rsYreozNUKaDjsh5kOXhjxuQlO5u8Qw6u5sH+4uACOU88PYsvOZx0KefeqrBtxW06HHhbzujp+GXBhB9QQrx9WGPGnJMYNoLc6tvnP144NoC4aOa82RbTRdZ2ZrKz119RdDXvTKt4dc0zStbH0Zl6mFOXPZOZn8suLyzmeeCOu2PBxrFzevfDQ2nqmlwfPr63gYymMhpXg89HUspOR4YGDKMpVWb01NTenQQw9NK1asSLNmzUodHR1p+/btYZsNGzY8b02SP6mtrU2TJk0K/wAAAAAAAPqr7AMjLS0t6amnnkqzZ89OJ5xwQho3bly68cYbi+ufeOKJtHr16nTyySeXuysAAAAAAEDOlXwqrQ984APp1a9+dVq4cGFau3Zt+shHPpKqqqrSm9/85tTY2Jj+6q/+Kl122WVpypQpadKkSemSSy5JJ598cnrRi15U6q4AAAAAAAAEJR8YWbNmTXrzm9+ctmzZkqZPn55OPfXUdM8996Tp06enlFL693//91RZWZkuuOCC1N7ens4999z0H//xH6XuxuhljrsxxbyvlFJt0/SQZ5/26uLypIOOHuruMIL1dHQMdxeAEcr5YWzJzgPeum5VyA2zFx1wW9UNE0OefcorQ15724/71bdymnP6a0KuqqkbcFs7Vvw+5K7WlgG3BUPJtSbl0rZ5bchPXvVvIc944dnF5WnHnxHWVVaXu7ZKRXFp4sLDw5ps7tixJeSNv/11yNse+23IhZ6eUnRwzOl9PPR1LKQ01MdDRUiOBwai5AMj3/ve9/pcX1dXl6688sp05ZVXlnrXAAAAAAAAfSp7jREAAAAAAICRwsAIAAAAAACQGyWfSgvYw7yvDMakxUeGvOAVbwu5/PO37lt3267icueunWFd1+44N3d3++6QezrbY87Md9/T2RZy3fS5IU9ccFj/OptH6lUB++L8MKatv+sXIR90wf/KbFGRDtS0F8R5wiuqa/rcV+/PBoNVXT8+5NmnvjrkyUeeNOC2C93dIWdfB4wWrjUZKj1d8Xpt/d17zptbHr4jrJt+4ktCnnLUspArx9WWuHf7VtM4NeR5Z78x5BknvjTkNTf/MOSW1U+Wp2OjWF/HQkpj93hwLIxd7hgBAAAAAAByw8AIAAAAAACQGwZGAAAAAACA3FBjBMqp5PN4x/ZWfO9zmbXmDe+vrpYdw92FoomLjgh54aveEXJFZenGsjt3NYe8Y8VDIe98+tGQd29+LuSu1lhHpJymH39WyGqMAMDza1mzIuTND9wWcrZuSH9MPebkkCcfcULIu557OuT2rRtC7u7YU0OsclxdWFc3dWbI4+cuCbmUddWe/fX3Qm7ftrFkbcOQKum1puvMUhtJ15nllL2uXHvrtSFvuOeGkKccFWtETTn6xSHXTp5eus7tR03TtJAPeu17Q97yUKyX0fu1FQo9ZevXaDZWj4e+joWUHA+jmTtGAAAAAACA3DAwAgAAAAAA5IaptKCMero6StxiRUjt2+Ot/93tbYnRo6o2TiMx7+w3hjyoqbMyt9avv+f6kDfdd3PcvLtr4PsCAEaktbf/JOTKcbXF5SlHv2hQbVdW14Q8ceHhfeah1NPVGfLaW64pLm9//L6h7g6URWmvNV1nUh7d7btD3nT/rX3mCfMODjn7XtV48LHF5Yqq8n6lOXXpqSFXNUwoLq/+xbfKuu+xajDHQ1/HQkrlPR76OhZScjyMZu4YAQAAAAAAcsPACAAAAAAAkBsGRgAAAAAAgNxQYwTKqHNXc1nbr6obH7K5X0eXaUtPD3nc+Ekla3v1L78b8qieT7uiYv/bAAB7y9QcW3PjD4rLLWtWhHWzT3llyOMmTi5fvwYtvq6dzzwR8ro7fhpy2+Z1Ze8RDLVyXmu6zmS4ZN+bsrm6YWJxeVqm7sPUpaeFnK3pOVhNhxxXXN51TOzXlt/fVdJ98Ue9f/99HQspDe3x0PtYSMnxMJq5YwQAAAAAAMgNAyMAAAAAAEBuGBgBAAAAAAByQ40RKKPOlu0hF3p6Qq6oHNzYZN2UmSF37NgyqPYYWo2HLi1ZW81P/yHkUV1TJKOqtn64uwAAY06hqzPkypp+zr2dqV/S2bIj5OqGCXH7Xp97ezraw6ruttaQ27ZuCLl17cqQm59+pM/tIQ/6utZ0nclY1dW6s7i8/u7rwrpND9wa8qwXvyLkqce8uGT9mLHsnJC3PnpvyIXu7pLti+fX+1hIyfHAwLhjBAAAAAAAyA0DIwAAAAAAQG4YGAEAAAAAAHJDjREoo+w8gu1b14dcN23OoNqvn7kg5OaVjw6qPcorWyujburskrW99dHflKytkaa6fvxwdwEAxoRpx51eXJ5zxvn9em62hsjq674V8q5MHRCgvPq61nSdSR5l61U9d9MPQ27btDbkuS95/YD3NW78pJDHzzko5JZnlw+4bUrD8cCBcMcIAAAAAACQGwZGAAAAAACA3DAwAgAAAAAA5IYaIzCEWp57KuTBzv06ccGhIW+45/pBtUd5ZeedLKXdG54tW9vDrWHWgv1vBADsZULms+KcM/7sgJ/b09Ee8tPX/EfI7ds2DbxjQMn1vtZ0nQl72/L7u0JuPGRpyBPmHzLgtutnzA9ZTYmRz/FASu4YAQAAAAAAcsTACAAAAAAAkBsGRgAAAAAAgNxQYwSGUPPTfwh52tLTBtVew+xFIddNmVlcbtu6YVBtU3qVtfVla7u7bVfZ2h5q1Q0TQ66bOnuYegIAo9usk8/LPFJxwM/d8vCdIaspAiNb72vNcl5npuRak7Fh56pHQx5MTYnq+obBdodh5njIJ3eMAAAAAAAAuWFgBAAAAAAAyA0DIwAAAAAAQG6oMQJDqOXZ5SF3NG8NuWbSlEG1P2PZOcXl1dd9e1BtUXo9HW1la7tyXE3cV1dn2fZVbtOOy8yJXHHg86EDQJ5VVsfPAw2zFgy4rdaNzw62O8AQ6n2tWc7rzJRca0JWd3v5rvUZfRwPo4c7RgAAAAAAgNwwMAIAAAAAAOSGgREAAAAAACA31BiBoVQohLjpvptDnnvWBYNqvunQFxSXdyx/KKzbseLhQbXN4HXu2lm2tuumzg65Zc2Ksu2r1Gqbpoc8belp+9gSAOhLVf34zCMDr9PVMHN+yNnPlsAI0+tas5zXmSm51mRsqJ+5sGRtdbbsKFlbDA/HQz65YwQAAAAAAMgNAyMAAAAAAEBumEoLhtHWR+4JeerSU0OumzJzwG0vePlbQl593bdD3vHU7wfc9khWVdcQ8oR5B4c8nLd5d7ftCrl926aQayfHKaX6o+nwE0IeyVNpVTdMCHnhq94ecmVN7RD2BgDGjq5dzSEXurtDrqiqOuC2ph9/VsiV4+L787bH7wu5feuGkLs72mKDmSllgfIp53VmSn1fa47V68yU+r7WHMnTiWV/35U1dSG3rn9mKLszbOqnzw25cckxg2gtvqe1PLt8EG0NLcfDHzkeSMkdIwAAAAAAQI4YGAEAAAAAAHLDwAgAAAAAAJAbaozAMCr0xHmfn73hqpAPvvBvQq6oOvA/2ey22ToO2x6/P+QN91wfcseOLQe8r3KrmTQl5AkLDg154sLDi8uTFh8V1rVvj3U8RtLcrztXPRbyYGqMTDnypNj2ykdDHs65fsfPXRLygnP/PORxEycPZXcAYMzKfrbcvvyBkCcffuKBN1ZREeLUY0/pMw+lQndXyN3tu0PuzNRa2b1xTXE5O+939rNhtm0Yjcp5nfl82/e+1hyr15kp9X2tOZKuM7Pqps0JecF5fxFy25b1IW/P1JBqzly3tm1eW8LelVJ832o8ONaMmPfSN8St+1F3K6tlzVMhd7ZsH3BbQ20wx8PoORZScjxwINwxAgAAAAAA5IaBEQAAAAAAIDcMjAAAAAAAALmhxgiMIL3nP04ppWd/9b2QF5x7UXxCZu7nvsVtJx9+Qp+5beuGkHc993TIXa1x7ubutj1zO1dUx1NL1bi6kKsbxodcO3lGn7m6YWIaizY9cEvI2bm6+zXHZeZYWPjKt4e89dHfhLztsd+F3LbpuZC7O9pDrqqpDbl6/KTicraGSNOhx4U8Yf4hz9/nA7Tl4TtDnnrMi/eEfv0NwOjUMHtRyBPmHRxyVW08x1bW1Pe5vqomk2vj9pW91mfX7bWvcfHcMFiH/sUHi8s9HW1hXbaGQHdmfU97W5/rs8/vyZznujv2rO9q3RnWbXn4rr66DSPW2lt/HHLDzAXF5eznrdEkW98g+1kxm+unzy0uTzlqWVjX3dYa8vpMPYQtD8XPISkV+tPVIdV0yHEh107Z8zsu5XvD8z5/r/eLmAcj+/s8+n9dEfJe5/teuWc/7x39fS/Jrt+dmV+/+elH0khU3uvMlHpfa5bzOjOlvq81XWcOTN3UWSHPOuWVfeben5Na160K67L1Kdq2bQw5e87Nfj7LqhxXE3JNr/qUddNmh3UTFx0Rcm3TwGt4Pp/etXvW3vKjkrY9kvR1PPR1LKRU3uOhr2MhpaE9HrJ1nMby8TDWuWMEAAAAAADIDQMjAAAAAABAbhgYAQAAAAAAckONERjBtj9xf5/r57/szSH3qybFftRNmdlnZvA6d24PeUNmTuvs/J39kpkXODufdjaPJNm5/J+7+X9C7j0/ev3M+UPSJxhOkxYdGfKMk84epp6UX2X1uOddTmlo5wHvbtsVshojjFbZY3nVT/9fcXnx+e8O62omTRmSPo00VXUNIc8983UhZ+s6rf7Ft0IuFHrK07EBmHrcaSGPn7N4mHpSXpWZ2nfZPC41Dllfmlc+GvMIrTGS5TqTwej9mWzSkmPCumwe1QqxptRzN/2wuNy2Zd1Q92ZEyn4+H7PHQx/HQkqOh9HMHSMAAAAAAEBuGBgBAAAAAAByw8AIAAAAAACQG2qMwCiSnQu2ffumkBece1FxuXbyjCHp04g3guZ93p+Nv7sx5HGZub6nHnPyUHanjOL8nBt/8+uQ1999XZ/P3vnME8VlNUYAYI/xcw8KeeZJ54Q8YcGhQ9mdMaHx4GNDnnXqq0Ned/uPh7I7UBb9uc5MybVm0Si61mT/utt3h7zmxh+EvGP5Q0PZHYZZ7+PBsTB2uWMEAAAAAADIDQMjAAAAAABAbhgYAQAAAAAAckONERjFdm94NuQnv/N/i8tTMvUoph9/Zsg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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.imshow(wordcloud_negative)\n",
    "plt.title(\"negative words\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.imshow(wordcloud_neutral)\n",
    "plt.title(\"neutral words\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SUMMARY OF STEVE JOBS SPEECH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: spacy in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.7.5)\n",
      "Collecting spacy\n",
      "  Downloading spacy-3.7.6-cp312-cp312-win_amd64.whl.metadata (27 kB)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (3.0.12)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (1.0.5)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (1.0.10)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (2.0.8)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (3.0.9)\n",
      "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (8.2.5)\n",
      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (1.1.3)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (2.4.8)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (2.0.10)\n",
      "Requirement already satisfied: weasel<0.5.0,>=0.1.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (0.4.1)\n",
      "Requirement already satisfied: typer<1.0.0,>=0.3.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (0.12.3)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (4.66.4)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (2.32.3)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (2.8.2)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (3.1.4)\n",
      "Requirement already satisfied: setuptools in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (72.1.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\subha\\appdata\\roaming\\python\\python312\\site-packages (from spacy) (24.1)\n",
      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (3.4.0)\n",
      "Requirement already satisfied: numpy>=1.19.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from spacy) (1.26.4)\n",
      "Requirement already satisfied: language-data>=1.2 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from langcodes<4.0.0,>=3.2.0->spacy) (1.2.0)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.20.1 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (2.20.1)\n",
      "Requirement already satisfied: typing-extensions>=4.6.1 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (4.12.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy) (2024.7.4)\n",
      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from thinc<8.3.0,>=8.2.2->spacy) (0.7.11)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from thinc<8.3.0,>=8.2.2->spacy) (0.1.5)\n",
      "Requirement already satisfied: colorama in c:\\users\\subha\\appdata\\roaming\\python\\python312\\site-packages (from tqdm<5.0.0,>=4.38.0->spacy) (0.4.6)\n",
      "Requirement already satisfied: click>=8.0.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from typer<1.0.0,>=0.3.0->spacy) (8.1.7)\n",
      "Requirement already satisfied: shellingham>=1.3.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from typer<1.0.0,>=0.3.0->spacy) (1.5.4)\n",
      "Requirement already satisfied: rich>=10.11.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from typer<1.0.0,>=0.3.0->spacy) (13.7.1)\n",
      "Requirement already satisfied: cloudpathlib<1.0.0,>=0.7.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from weasel<0.5.0,>=0.1.0->spacy) (0.18.1)\n",
      "Requirement already satisfied: smart-open<8.0.0,>=5.2.1 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from weasel<0.5.0,>=0.1.0->spacy) (7.0.4)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\subha\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from jinja2->spacy) (2.1.5)\n",
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      "Installing collected packages: spacy\n",
      "  Attempting uninstall: spacy\n",
      "    Found existing installation: spacy 3.7.5\n",
      "    Uninstalling spacy-3.7.5:\n",
      "      Successfully uninstalled spacy-3.7.5\n",
      "Successfully installed spacy-3.7.6\n"
     ]
    }
   ],
   "source": [
    "!pip install -U spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'pyhon' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!pyhon -m spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "from spacy.lang.en.stop_words import STOP_WORDS\n",
    "from string import punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I reached the pinnacle of success in the business world. In others’ eyes, my life is the epitome of success. However, aside from work, I have little joy. In the end, my wealth is only a fact of life that I am accustomed to. At this moment, lying on my bed and recalling my life, I realize that all the recognition and wealth that I took so much pride in have paled and become meaningless in the face of my death.\\n\\nYou can employ someone to drive the car for you, make money for you, but you cannot have someone bear your sickness for you. Material things lost can be found or replaced. But there is one thing that can never be found when lost—Life. Whichever stage in life you’re in right now, with time, you will face the day when the curtain falls.\\n\\nTreasure love for your family, love for your spouse, love for your friends. Treat yourself well and cherish others. As we grow older, and hopefully wiser, we realize that a $3000 or a $30 watch both tell the same time. You will realize that your true inner happiness does not come from the material things of this world. Whether you fly first class or economy, if the plane goes down—you go down with it.\\n\\nTherefore, I hope you realize, when you have mates, buddies and old friends, brothers and sisters, who you chat with, laugh with, talk with, sing with, talk about north-south-east-west or heaven and earth, that is true happiness. Don’t educate your children to be rich. Educate them to be happy. So when they grow up they will know the value of things and not the price.\\n\\nEat your food as your medicine, otherwise, you have to eat medicine as your food.\\n\\nThe One who loves you will never leave you for another because, even if there are 100 reasons to give up, he or she will find a reason to hold on. There is a big difference between a human being and being human. Only a few really understand it. You are loved when you are born. You will be loved when you die. In between, you have to manage.\\n\\nThe six best doctors in the world are sunlight, rest, exercise, diet, self-confidence, and friends. Maintain them in all stages and enjoy a healthy life.'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlpp=spacy.load('en_core_web_md')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc=nlpp(steve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "I reached the pinnacle of success in the business world. In others’ eyes, my life is the epitome of success. However, aside from work, I have little joy. In the end, my wealth is only a fact of life that I am accustomed to. At this moment, lying on my bed and recalling my life, I realize that all the recognition and wealth that I took so much pride in have paled and become meaningless in the face of my death.\n",
       "\n",
       "You can employ someone to drive the car for you, make money for you, but you cannot have someone bear your sickness for you. Material things lost can be found or replaced. But there is one thing that can never be found when lost—Life. Whichever stage in life you’re in right now, with time, you will face the day when the curtain falls.\n",
       "\n",
       "Treasure love for your family, love for your spouse, love for your friends. Treat yourself well and cherish others. As we grow older, and hopefully wiser, we realize that a $3000 or a $30 watch both tell the same time. You will realize that your true inner happiness does not come from the material things of this world. Whether you fly first class or economy, if the plane goes down—you go down with it.\n",
       "\n",
       "Therefore, I hope you realize, when you have mates, buddies and old friends, brothers and sisters, who you chat with, laugh with, talk with, sing with, talk about north-south-east-west or heaven and earth, that is true happiness. Don’t educate your children to be rich. Educate them to be happy. So when they grow up they will know the value of things and not the price.\n",
       "\n",
       "Eat your food as your medicine, otherwise, you have to eat medicine as your food.\n",
       "\n",
       "The One who loves you will never leave you for another because, even if there are 100 reasons to give up, he or she will find a reason to hold on. There is a big difference between a human being and being human. Only a few really understand it. You are loved when you are born. You will be loved when you die. In between, you have to manage.\n",
       "\n",
       "The six best doctors in the world are sunlight, rest, exercise, diet, self-confidence, and friends. Maintain them in all stages and enjoy a healthy life."
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = list(STOP_WORDS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most frequent word appeared 6 times.\n"
     ]
    }
   ],
   "source": [
    "punctuation = punctuation + '\\n' \n",
    "word_frequencies = {}\n",
    "for word in doc:\n",
    "    if word.text.lower() not in stopwords:\n",
    "        if word.text.lower() not in punctuation:\n",
    "            if word.text not in word_frequencies.keys():\n",
    "                word_frequencies[word.text] = 1\n",
    "            else:\n",
    "                word_frequencies[word.text] += 1\n",
    "max_frequency = max(word_frequencies.values())\n",
    "print(\"The most frequent word appeared {} times.\".format(max_frequency))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reached': 1,\n",
       " 'pinnacle': 1,\n",
       " 'success': 2,\n",
       " 'business': 1,\n",
       " 'world': 3,\n",
       " '’': 1,\n",
       " 'eyes': 1,\n",
       " 'life': 5,\n",
       " 'epitome': 1,\n",
       " 'aside': 1,\n",
       " 'work': 1,\n",
       " 'little': 1,\n",
       " 'joy': 1,\n",
       " 'end': 1,\n",
       " 'wealth': 2,\n",
       " 'fact': 1,\n",
       " 'accustomed': 1,\n",
       " 'moment': 1,\n",
       " 'lying': 1,\n",
       " 'bed': 1,\n",
       " 'recalling': 1,\n",
       " 'realize': 4,\n",
       " 'recognition': 1,\n",
       " 'took': 1,\n",
       " 'pride': 1,\n",
       " 'paled': 1,\n",
       " 'meaningless': 1,\n",
       " 'face': 2,\n",
       " 'death': 1,\n",
       " '\\n\\n': 6,\n",
       " 'employ': 1,\n",
       " 'drive': 1,\n",
       " 'car': 1,\n",
       " 'money': 1,\n",
       " 'bear': 1,\n",
       " 'sickness': 1,\n",
       " 'Material': 1,\n",
       " 'things': 3,\n",
       " 'lost': 2,\n",
       " 'found': 2,\n",
       " 'replaced': 1,\n",
       " 'thing': 1,\n",
       " '—': 2,\n",
       " 'Life': 1,\n",
       " 'Whichever': 1,\n",
       " 'stage': 1,\n",
       " 'right': 1,\n",
       " 'time': 2,\n",
       " 'day': 1,\n",
       " 'curtain': 1,\n",
       " 'falls': 1,\n",
       " 'Treasure': 1,\n",
       " 'love': 3,\n",
       " 'family': 1,\n",
       " 'spouse': 1,\n",
       " 'friends': 3,\n",
       " 'Treat': 1,\n",
       " 'cherish': 1,\n",
       " 'grow': 2,\n",
       " 'older': 1,\n",
       " 'hopefully': 1,\n",
       " 'wiser': 1,\n",
       " '3000': 1,\n",
       " '30': 1,\n",
       " 'watch': 1,\n",
       " 'tell': 1,\n",
       " 'true': 2,\n",
       " 'inner': 1,\n",
       " 'happiness': 2,\n",
       " 'come': 1,\n",
       " 'material': 1,\n",
       " 'fly': 1,\n",
       " 'class': 1,\n",
       " 'economy': 1,\n",
       " 'plane': 1,\n",
       " 'goes': 1,\n",
       " 'hope': 1,\n",
       " 'mates': 1,\n",
       " 'buddies': 1,\n",
       " 'old': 1,\n",
       " 'brothers': 1,\n",
       " 'sisters': 1,\n",
       " 'chat': 1,\n",
       " 'laugh': 1,\n",
       " 'talk': 2,\n",
       " 'sing': 1,\n",
       " 'north': 1,\n",
       " 'south': 1,\n",
       " 'east': 1,\n",
       " 'west': 1,\n",
       " 'heaven': 1,\n",
       " 'earth': 1,\n",
       " 'educate': 1,\n",
       " 'children': 1,\n",
       " 'rich': 1,\n",
       " 'Educate': 1,\n",
       " 'happy': 1,\n",
       " 'know': 1,\n",
       " 'value': 1,\n",
       " 'price': 1,\n",
       " 'Eat': 1,\n",
       " 'food': 2,\n",
       " 'medicine': 2,\n",
       " 'eat': 1,\n",
       " 'loves': 1,\n",
       " 'leave': 1,\n",
       " '100': 1,\n",
       " 'reasons': 1,\n",
       " 'find': 1,\n",
       " 'reason': 1,\n",
       " 'hold': 1,\n",
       " 'big': 1,\n",
       " 'difference': 1,\n",
       " 'human': 2,\n",
       " 'understand': 1,\n",
       " 'loved': 2,\n",
       " 'born': 1,\n",
       " 'die': 1,\n",
       " 'manage': 1,\n",
       " 'best': 1,\n",
       " 'doctors': 1,\n",
       " 'sunlight': 1,\n",
       " 'rest': 1,\n",
       " 'exercise': 1,\n",
       " 'diet': 1,\n",
       " 'self': 1,\n",
       " 'confidence': 1,\n",
       " 'Maintain': 1,\n",
       " 'stages': 1,\n",
       " 'enjoy': 1,\n",
       " 'healthy': 1}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "for word in word_frequencies.keys():\n",
    "    word_frequencies[word] = word_frequencies[word]/max_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I reached the pinnacle of success in the business world., In others’ eyes, my life is the epitome of success., However, aside from work, I have little joy., In the end, my wealth is only a fact of life that I am accustomed to., At this moment, lying on my bed and recalling my life, I realize that all the recognition and wealth that I took so much pride in have paled and become meaningless in the face of my death.\n",
      "\n",
      ", You can employ someone to drive the car for you, make money for you, but you cannot have someone bear your sickness for you., Material things lost can be found or replaced., But there is one thing that can never be found when lost—Life., Whichever stage in life you’re in right now, with time, you will face the day when the curtain falls.\n",
      "\n",
      ", Treasure love for your family, love for your spouse, love for your friends., Treat yourself well and cherish others., As we grow older, and hopefully wiser, we realize that a $3000 or a $30 watch both tell the same time., You will realize that your true inner happiness does not come from the material things of this world., Whether you fly first class or economy, if the plane goes down—you go down with it.\n",
      "\n",
      ", Therefore, I hope you realize, when you have mates, buddies and old friends, brothers and sisters, who you chat with, laugh with, talk with, sing with, talk about north-south-east-west or heaven and earth, that is true happiness., Don’t educate your children to be rich., Educate them to be happy., So when they grow up they will know the value of things and not the price.\n",
      "\n",
      ", Eat your food as your medicine, otherwise, you have to eat medicine as your food.\n",
      "\n",
      ", The One who loves you will never leave you for another because, even if there are 100 reasons to give up, he or she will find a reason to hold on., There is a big difference between a human being and being human., Only a few really understand it., You are loved when you are born., You will be loved when you die., In between, you have to manage.\n",
      "\n",
      ", The six best doctors in the world are sunlight, rest, exercise, diet, self-confidence, and friends., Maintain them in all stages and enjoy a healthy life.]\n"
     ]
    }
   ],
   "source": [
    "sentence_tokens = [sent for sent in doc.sents]\n",
    "print(sentence_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{I reached the pinnacle of success in the business world.: 1.3333333333333333,\n",
       " In others’ eyes, my life is the epitome of success.: 1.6666666666666667,\n",
       " However, aside from work, I have little joy.: 0.6666666666666666,\n",
       " In the end, my wealth is only a fact of life that I am accustomed to.: 1.6666666666666667,\n",
       " At this moment, lying on my bed and recalling my life, I realize that all the recognition and wealth that I took so much pride in have paled and become meaningless in the face of my death.\n",
       " : 4.833333333333332,\n",
       " You can employ someone to drive the car for you, make money for you, but you cannot have someone bear your sickness for you.: 0.9999999999999999,\n",
       " Material things lost can be found or replaced.: 1.5,\n",
       " But there is one thing that can never be found when lost—Life.: 2.0,\n",
       " Whichever stage in life you’re in right now, with time, you will face the day when the curtain falls.\n",
       " : 3.333333333333333,\n",
       " Treasure love for your family, love for your spouse, love for your friends.: 2.333333333333333,\n",
       " Treat yourself well and cherish others.: 0.16666666666666666,\n",
       " As we grow older, and hopefully wiser, we realize that a $3000 or a $30 watch both tell the same time.: 2.5,\n",
       " You will realize that your true inner happiness does not come from the material things of this world.: 2.8333333333333335,\n",
       " Whether you fly first class or economy, if the plane goes down—you go down with it.\n",
       " : 2.1666666666666665,\n",
       " Therefore, I hope you realize, when you have mates, buddies and old friends, brothers and sisters, who you chat with, laugh with, talk with, sing with, talk about north-south-east-west or heaven and earth, that is true happiness.: 4.999999999999999,\n",
       " Don’t educate your children to be rich.: 0.5,\n",
       " Educate them to be happy.: 0.3333333333333333,\n",
       " So when they grow up they will know the value of things and not the price.\n",
       " : 2.333333333333333,\n",
       " Eat your food as your medicine, otherwise, you have to eat medicine as your food.\n",
       " : 2.6666666666666665,\n",
       " The One who loves you will never leave you for another because, even if there are 100 reasons to give up, he or she will find a reason to hold on.: 1.1666666666666665,\n",
       " There is a big difference between a human being and being human.: 1.0,\n",
       " Only a few really understand it.: 0.16666666666666666,\n",
       " You are loved when you are born.: 0.5,\n",
       " You will be loved when you die.: 0.5,\n",
       " In between, you have to manage.\n",
       " : 1.1666666666666667,\n",
       " The six best doctors in the world are sunlight, rest, exercise, diet, self-confidence, and friends.: 2.3333333333333335,\n",
       " Maintain them in all stages and enjoy a healthy life.: 1.3333333333333335}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_scores = {}\n",
    "for sent in sentence_tokens:\n",
    "    for word in sent:\n",
    "        if word.text.lower() in word_frequencies.keys():\n",
    "            if sent not in sentence_scores.keys():\n",
    "                sentence_scores[sent] = word_frequencies[word.text.lower()]\n",
    "            else:\n",
    "                sentence_scores[sent] += word_frequencies[word.text.lower()]\n",
    "sentence_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from heapq import nlargest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_length = int(len(sentence_tokens)*0.2)\n",
    "select_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Therefore, I hope you realize, when you have mates, buddies and old friends, brothers and sisters, who you chat with, laugh with, talk with, sing with, talk about north-south-east-west or heaven and earth, that is true happiness.,\n",
       " At this moment, lying on my bed and recalling my life, I realize that all the recognition and wealth that I took so much pride in have paled and become meaningless in the face of my death.\n",
       " ,\n",
       " Whichever stage in life you’re in right now, with time, you will face the day when the curtain falls.\n",
       " ,\n",
       " You will realize that your true inner happiness does not come from the material things of this world.,\n",
       " Eat your food as your medicine, otherwise, you have to eat medicine as your food.\n",
       " ]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = nlargest(select_length, sentence_scores, key = sentence_scores.get)\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Therefore, I hope you realize, when you have mates, buddies and old friends, brothers and sisters, who you chat with, laugh with, talk with, sing with, talk about north-south-east-west or heaven and earth, that is true happiness. At this moment, lying on my bed and recalling my life, I realize that all the recognition and wealth that I took so much pride in have paled and become meaningless in the face of my death.\n",
      "\n",
      " Whichever stage in life you’re in right now, with time, you will face the day when the curtain falls.\n",
      "\n",
      " You will realize that your true inner happiness does not come from the material things of this world. Eat your food as your medicine, otherwise, you have to eat medicine as your food.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "final_summary = [word.text for word in summary]\n",
    "summary = ' '.join(final_summary)\n",
    "print(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word count of the original paper:  2109\n",
      "Word count after summerization:  710\n"
     ]
    }
   ],
   "source": [
    "print(\"Word count of the original paper: \", len(steve))\n",
    "print(\"Word count after summerization: \", len(summary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
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 },
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