Sentiment analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /Users/sahil/anaconda3/lib/python3.7/site-packages (0.9.0)\n",
      "Requirement already satisfied: pandas>=0.15.2 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from seaborn) (0.23.4)\n",
      "Requirement already satisfied: numpy>=1.9.3 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from seaborn) (1.15.4)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from seaborn) (1.1.0)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from seaborn) (3.0.2)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from pandas>=0.15.2->seaborn) (2.7.5)\n",
      "Requirement already satisfied: pytz>=2011k in /Users/sahil/anaconda3/lib/python3.7/site-packages (from pandas>=0.15.2->seaborn) (2018.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (1.0.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (2.3.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.5.0->pandas>=0.15.2->seaborn) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /Users/sahil/anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn) (40.6.3)\n",
      "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: nltk in /Users/sahil/anaconda3/lib/python3.7/site-packages (3.4)\n",
      "Requirement already satisfied: six in /Users/sahil/anaconda3/lib/python3.7/site-packages (from nltk) (1.12.0)\n",
      "Requirement already satisfied: singledispatch in /Users/sahil/anaconda3/lib/python3.7/site-packages (from nltk) (3.4.0.3)\n",
      "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: pandas in /Users/sahil/anaconda3/lib/python3.7/site-packages (0.23.4)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from pandas) (2.7.5)\n",
      "Requirement already satisfied: pytz>=2011k in /Users/sahil/anaconda3/lib/python3.7/site-packages (from pandas) (2018.7)\n",
      "Requirement already satisfied: numpy>=1.9.0 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from pandas) (1.15.4)\n",
      "Requirement already satisfied: six>=1.5 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n",
      "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: numpy in /Users/sahil/anaconda3/lib/python3.7/site-packages (1.15.4)\n",
      "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: matplotlib in /Users/sahil/anaconda3/lib/python3.7/site-packages (3.0.2)\n",
      "Requirement already satisfied: numpy>=1.10.0 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from matplotlib) (1.15.4)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from matplotlib) (1.0.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from matplotlib) (2.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from matplotlib) (2.7.5)\n",
      "Requirement already satisfied: six in /Users/sahil/anaconda3/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /Users/sahil/anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (40.6.3)\n",
      "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: IPython in /Users/sahil/anaconda3/lib/python3.7/site-packages (7.2.0)\n",
      "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (2.0.7)\n",
      "Requirement already satisfied: jedi>=0.10 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (0.13.2)\n",
      "Requirement already satisfied: decorator in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (4.3.0)\n",
      "Requirement already satisfied: appnope; sys_platform == \"darwin\" in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (0.1.0)\n",
      "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (4.6.0)\n",
      "Requirement already satisfied: backcall in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (0.1.0)\n",
      "Requirement already satisfied: setuptools>=18.5 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (40.6.3)\n",
      "Requirement already satisfied: pickleshare in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (0.7.5)\n",
      "Requirement already satisfied: traitlets>=4.2 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (4.3.2)\n",
      "Requirement already satisfied: pygments in /Users/sahil/anaconda3/lib/python3.7/site-packages (from IPython) (2.3.1)\n",
      "Requirement already satisfied: six>=1.9.0 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from prompt-toolkit<2.1.0,>=2.0.0->IPython) (1.12.0)\n",
      "Requirement already satisfied: wcwidth in /Users/sahil/anaconda3/lib/python3.7/site-packages (from prompt-toolkit<2.1.0,>=2.0.0->IPython) (0.1.7)\n",
      "Requirement already satisfied: parso>=0.3.0 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from jedi>=0.10->IPython) (0.3.1)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /Users/sahil/anaconda3/lib/python3.7/site-packages (from pexpect; sys_platform != \"win32\"->IPython) (0.6.0)\n",
      "Requirement already satisfied: ipython-genutils in /Users/sahil/anaconda3/lib/python3.7/site-packages (from traitlets>=4.2->IPython) (0.2.0)\n",
      "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "\u001b[31mERROR: Could not find a version that satisfies the requirement math (from versions: none)\u001b[0m\n",
      "\u001b[31mERROR: No matching distribution found for math\u001b[0m\n",
      "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: pprint in /Users/sahil/anaconda3/lib/python3.7/site-packages (0.1)\n",
      "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn\n",
    "!pip install nltk\n",
    "!pip install pandas\n",
    "!pip install numpy\n",
    "!pip install matplotlib\n",
    "!pip install IPython\n",
    "!pip install math\n",
    "!pip install pprint\n",
    "from IPython import display\n",
    "import math\n",
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid', context='talk', palette='Dark2')\n",
    "import csv\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"news.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Title</th>\n",
       "      <th>URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Trudeau announces $62.5 million for seafood, f...</td>\n",
       "      <td>https://news.google.com/articles/CAIiEOlxnklWR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coronavirus: Disinfectant riff the latest of T...</td>\n",
       "      <td>https://news.google.com/articles/CAIiEJ1tWZV1u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coronavirus: What's happening in Canada and ar...</td>\n",
       "      <td>https://news.google.com/articles/CBMiPGh0dHBzO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Is it safe to go running during the coronaviru...</td>\n",
       "      <td>https://news.google.com/articles/CAIiEBFqvVALO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coronavirus: Brazil emerging as world hot spot...</td>\n",
       "      <td>https://news.google.com/articles/CAIiEHqq55m3D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Disinfectant Against Coronavirus: How to Make ...</td>\n",
       "      <td>https://news.google.com/articles/CAIiEKc90rfvP...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Is the curve flattening? Expert says coronavir...</td>\n",
       "      <td>https://news.google.com/articles/CAIiEP5JBHOLe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>'We knew that the foreign workers would be a s...</td>\n",
       "      <td>https://news.google.com/articles/CAIiEGeTamD-M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Virginia sees 772 new coronavirus cases, now r...</td>\n",
       "      <td>https://news.google.com/articles/CAIiECzCJ22j9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Canada could see 22,000 coronavirus deaths eve...</td>\n",
       "      <td>https://news.google.com/articles/CAIiEPIubUrOO...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Title  \\\n",
       "0   Trudeau announces $62.5 million for seafood, f...   \n",
       "1   Coronavirus: Disinfectant riff the latest of T...   \n",
       "2   Coronavirus: What's happening in Canada and ar...   \n",
       "3   Is it safe to go running during the coronaviru...   \n",
       "4   Coronavirus: Brazil emerging as world hot spot...   \n",
       "..                                                ...   \n",
       "95  Disinfectant Against Coronavirus: How to Make ...   \n",
       "96  Is the curve flattening? Expert says coronavir...   \n",
       "97  'We knew that the foreign workers would be a s...   \n",
       "98  Virginia sees 772 new coronavirus cases, now r...   \n",
       "99  Canada could see 22,000 coronavirus deaths eve...   \n",
       "\n",
       "                                                  URL  \n",
       "0   https://news.google.com/articles/CAIiEOlxnklWR...  \n",
       "1   https://news.google.com/articles/CAIiEJ1tWZV1u...  \n",
       "2   https://news.google.com/articles/CBMiPGh0dHBzO...  \n",
       "3   https://news.google.com/articles/CAIiEBFqvVALO...  \n",
       "4   https://news.google.com/articles/CAIiEHqq55m3D...  \n",
       "..                                                ...  \n",
       "95  https://news.google.com/articles/CAIiEKc90rfvP...  \n",
       "96  https://news.google.com/articles/CAIiEP5JBHOLe...  \n",
       "97  https://news.google.com/articles/CAIiEGeTamD-M...  \n",
       "98  https://news.google.com/articles/CAIiECzCJ22j9...  \n",
       "99  https://news.google.com/articles/CAIiEPIubUrOO...  \n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles =  df['Title'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Trudeau announces $62.5 million for seafood, fish processors during coronavirus pandemic',\n",
       " 'Coronavirus: Disinfectant riff the latest of Trump’s many clashes with science',\n",
       " \"Coronavirus: What's happening in Canada and around the world on Saturday\",\n",
       " 'Is it safe to go running during the coronavirus pandemic?',\n",
       " 'Coronavirus: Brazil emerging as world hot spot for virus',\n",
       " 'Trump’s role in coronavirus briefings may be reduced after disinfectant remarks: sources',\n",
       " 'The fight to save patients from the coronavirus at Surrey Memorial Hospital',\n",
       " 'Coronavirus: Canada adds over 1,700 new cases, 161 new deaths',\n",
       " 'Coronavirus: Asian-Americans turn to guns, GoPros to deter attacks',\n",
       " 'What you need to know about coronavirus on Saturday, April 25',\n",
       " 'Coronavirus: Plan to reopen Ontario economy will be revealed ‘early next week’',\n",
       " 'Coronavirus: 2 more confirmed cases in Interior Health region, total now 158',\n",
       " 'Conrad Black: A path out of the coronavirus lockdown',\n",
       " 'Emotional burden of coronavirus on British Columbians has been significant: poll',\n",
       " 'Ontario reports 640 new coronavirus cases, total reaches 13,519 with 763 deaths',\n",
       " 'Somali civilian shot dead by police amid coronavirus restrictions',\n",
       " \"Coronavirus: Belarus orphanage seeks help amid 'critical' outbreak\",\n",
       " 'Mandatory Face Masks And More: Canadian Airlines Introduce New Coronavirus Travel Safety Measures',\n",
       " 'Running is enjoying a boom because of the coronavirus pandemic',\n",
       " 'Coronavirus antibody testing a big deal? What to know about the promising serology tests',\n",
       " \"The United States' reported coronavirus deaths approach 52,000, over a quarter of the world's known tally\",\n",
       " \"Coronavirus: The 'good outcome' that never was\",\n",
       " 'The 5-month-old daughter of a New York firefighter dies from coronavirus',\n",
       " 'How the coronavirus pandemic has shaken the US military',\n",
       " \"'No Evidence' That Recovered COVID-19 Patients Are Immune, WHO Says\",\n",
       " \"'They are trying to steal everything.' US coronavirus response hit by foreign hackers\",\n",
       " 'Watch the entire CNN/Sesame Street coronavirus town hall',\n",
       " 'Coronavirus: Further 16 deaths confirmed in NI',\n",
       " 'After her husband died from coronavirus, she found an emotional goodbye note on his phone',\n",
       " \"Singapore's migrant workers are suffering the brunt of the country's coronavirus outbreak\",\n",
       " '10 leaders who mattered most on coronavirus response this week',\n",
       " 'Coronavirus, CARES And PPP Will Explode The Federal Deficit And Debt',\n",
       " 'Bleach and sunlight might kill the coronavirus on a park bench, but they can be harmful to the body',\n",
       " 'Coronavirus Has Infected A 5th Of New York City, Testing Suggests',\n",
       " '‘Worst is yet ahead of us’ in coronavirus outbreak, WHO warns',\n",
       " '‘COVID toes’ could be another symptom of coronavirus infection: experts',\n",
       " 'Martinsville-region COVID-19/coronavirus daily update from state, nation and world: April 25',\n",
       " 'Cleaning a floating petri dish: How is a cruise ship sanitized after a coronavirus outbreak?',\n",
       " 'Can coronavirus cases be deduced from sewage? An MIT start-up tried it in Delaware and the governor wasn’t imp',\n",
       " 'Citing a high risk of death, researchers cut chloroquine coronavirus study short over safety concerns',\n",
       " 'This 2-year-old boy was adopted over Zoom after coronavirus pandemic canceled court hearings',\n",
       " 'Coronavirus: Transport usage will change after lockdown',\n",
       " \"Nick Cordero's wife offers update as he battles coronavirus\",\n",
       " \"Coronavirus: Applause for key workers 'is not enough'\",\n",
       " \"Pelosi promises more coronavirus relief: 'There will be a bill and it will be expensive'\",\n",
       " \"U.S. Hospitals Hit By Financial 'Triple Whammy' During Coronavirus Pandemic\",\n",
       " \"'Science will come through': Doctor who pioneered HIV treatment discusses potential Covid-19 drugs\",\n",
       " 'Trump pulls punches and clings to his China trade deal as backlash against Beijing grows',\n",
       " 'N.J. Gov. Phil Murphy provides daily coronavirus update: How to watch live. (April 25, 2020)',\n",
       " '606 new coronavirus cases in Ontario, total reaches 11,184 cases and 584 deaths',\n",
       " \"Some states get a taste of normalcy but others take 'whatever steps necessary' to slow coronavirus spread\",\n",
       " \"Donald Trump said coronavirus 'should have never happened.' What?\",\n",
       " 'Leaked White House emails reveal behind-the-scenes battle over chloroquine in coronavirus response',\n",
       " \"Bill Gates: 'I wish I could say that we're halfway through' coronavirus pandemic\",\n",
       " 'Walmart store in Colorado shuts down after coronavirus deaths',\n",
       " 'Coronavirus detected on particles of air pollution',\n",
       " \"As States Open Up, Health Experts Seek To Understand Coronavirus's Early Silent Spread\",\n",
       " 'Why The US Early Warning System For Spread Of Coronavirus Stumbled : Shots - Health News',\n",
       " 'The authoritarian threats of coronavirus',\n",
       " \"Ask Dr. Nandi: Trump suggests 'injection' of disinfectant to beat coronavirus and 'clean' the lungs\",\n",
       " 'Canada is flattening the coronavirus curve. That’s ‘good news,’ expert explains',\n",
       " 'Global stocks falter as investors lose faith in potential coronavirus treatment',\n",
       " '2 Californians died of coronavirus weeks before previously known 1st US death',\n",
       " 'Michael Avenatti released from jail due to coronavirus fears',\n",
       " \"Do homemade face masks keep you from acquiring coronavirus? Here's what we know\",\n",
       " 'Answering Your Coronavirus Questions: Vaccines, Mythbusting And Dating',\n",
       " 'Coronavirus refunds: why fans are raging over ticket policies',\n",
       " 'Coronavirus The latest coronavirus updates: Saturday, April 25, 2020 9:08 AM, Apr 25, 2020',\n",
       " 'NASA team developed a ventilator tailored for coronavirus patients in 37 days',\n",
       " 'Coronavirus in Georgia | Early Saturday there were 22491 confirmed cases; nearly 900 deaths',\n",
       " 'Open science takes on the coronavirus pandemic Data sharing, open-source designs for medical equipment, and',\n",
       " 'A family on the frontlines',\n",
       " 'April 24 Coronavirus Data Posted – Total positives: 3699',\n",
       " 'As coronavirus crushes travel industry, Expedia maps out private equity survival strategy',\n",
       " \"Coronavirus spread 'under the radar' in US major cities since January, researchers say\",\n",
       " 'Coronavirus: Stories of unemployment, fear and hope in the US',\n",
       " \"Japanese mayor says men should grocery shop during pandemic as women 'take a longer time'\",\n",
       " \"Russia tries to stay neutral as US-China tensions rise over coronavirus' origin\",\n",
       " 'California launches meal delivery program for seniors, US coronavirus death toll tops 50,000',\n",
       " 'Virus Researchers Cast Doubt On Theory Of Coronavirus Lab Accident',\n",
       " 'Coronavirus study predicts no deaths after May 19, but experts are sceptical',\n",
       " 'Muslims Around The World Face A Different Kind Of Ramadan',\n",
       " 'Rudy Giuliani may have said the dumbest thing yet uttered about the coronavirus',\n",
       " \"He signed up for a coronavirus vaccine trial using a method that's never been used in humans. Here's why.\",\n",
       " \"Coronavirus pandemic will cause global famines of 'biblical proportions,' UN warns\",\n",
       " 'Some Southern states are reopening businesses; US death toll passes 42,000',\n",
       " 'Watch live: California Gov. Newsom gives coronavirus update | TheHill',\n",
       " 'Second US Navy warship hit by major coronavirus outbreak',\n",
       " 'Coronavirus: 241 new deaths, 217 hospital admissions in Belgium',\n",
       " 'Boeing pushes back target date for fixing 737 Max as coronavirus hinders progress',\n",
       " \"Inside Mark Hamilton's journey from MLB to MD fighting coronavirus\",\n",
       " 'Florida COVID-19 coronavirus cases, deaths, hospitalizations',\n",
       " 'The schoolboy brothers making coronavirus visors for care workers',\n",
       " 'Opinion | Who Has Enough Cash to Get Through the Coronavirus Crisis?',\n",
       " \"What you need to know about how the coronavirus spreads — it's not just about staying 6 feet apart\",\n",
       " 'Disinfectant Against Coronavirus: How to Make and Use',\n",
       " 'Is the curve flattening? Expert says coronavirus death numbers will tell us',\n",
       " \"'We knew that the foreign workers would be a stress point:' Singapore diplomat on battling coronavirus\",\n",
       " 'Virginia sees 772 new coronavirus cases, now reporting 12,366 statewide',\n",
       " 'Canada could see 22,000 coronavirus deaths even with physical distancing, officials say']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(titles))\n",
    "titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NLTK’s built-in Vader Sentiment Analyzer will simply rank a piece of text as positive, negative or neutral using a lexicon of positive and negative words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'compound': 0.0,\n",
      "  'headline': 'Trudeau announces $62.5 million for seafood, fish '\n",
      "              'processors during coronavirus pandemic',\n",
      "  'neg': 0.0,\n",
      "  'neu': 1.0,\n",
      "  'pos': 0.0},\n",
      " {'compound': 0.0,\n",
      "  'headline': 'Coronavirus: Disinfectant riff the latest of Trump’s '\n",
      "              'many clashes with science',\n",
      "  'neg': 0.0,\n",
      "  'neu': 1.0,\n",
      "  'pos': 0.0}]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/sahil/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer as SIA\n",
    "nltk.downloader.download('vader_lexicon')\n",
    "sia = SIA()\n",
    "results = []\n",
    "\n",
    "for headline in titles:\n",
    "    pol_score = sia.polarity_scores(headline)\n",
    "#     print(pol_score)\n",
    "    pol_score['headline'] = headline\n",
    "    results.append(pol_score)\n",
    "pprint(results[:2], width=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating labels for each headline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiments = pd.DataFrame.from_records(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>headline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Trudeau announces $62.5 million for seafood, f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Coronavirus: Disinfectant riff the latest of T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Coronavirus: What's happening in Canada and ar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>Is it safe to go running during the coronaviru...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Coronavirus: Brazil emerging as world hot spot...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   neg    neu    pos  compound  \\\n",
       "0  0.0  1.000  0.000    0.0000   \n",
       "1  0.0  1.000  0.000    0.0000   \n",
       "2  0.0  1.000  0.000    0.0000   \n",
       "3  0.0  0.756  0.244    0.4404   \n",
       "4  0.0  1.000  0.000    0.0000   \n",
       "\n",
       "                                            headline  \n",
       "0  Trudeau announces $62.5 million for seafood, f...  \n",
       "1  Coronavirus: Disinfectant riff the latest of T...  \n",
       "2  Coronavirus: What's happening in Canada and ar...  \n",
       "3  Is it safe to go running during the coronaviru...  \n",
       "4  Coronavirus: Brazil emerging as world hot spot...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>headline</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Trudeau announces $62.5 million for seafood, f...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Coronavirus: Disinfectant riff the latest of T...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Coronavirus: What's happening in Canada and ar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>Is it safe to go running during the coronaviru...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Coronavirus: Brazil emerging as world hot spot...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   neg    neu    pos  compound  \\\n",
       "0  0.0  1.000  0.000    0.0000   \n",
       "1  0.0  1.000  0.000    0.0000   \n",
       "2  0.0  1.000  0.000    0.0000   \n",
       "3  0.0  0.756  0.244    0.4404   \n",
       "4  0.0  1.000  0.000    0.0000   \n",
       "\n",
       "                                            headline  label  \n",
       "0  Trudeau announces $62.5 million for seafood, f...      0  \n",
       "1  Coronavirus: Disinfectant riff the latest of T...      0  \n",
       "2  Coronavirus: What's happening in Canada and ar...      0  \n",
       "3  Is it safe to go running during the coronaviru...      1  \n",
       "4  Coronavirus: Brazil emerging as world hot spot...      0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiments['label'] = 0\n",
    "sentiments.loc[sentiments['compound'] > 0.2, 'label'] = 1\n",
    "sentiments.loc[sentiments['compound'] < -0.2, 'label'] = -1\n",
    "sentiments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = sentiments[['headline', 'label']]\n",
    "df2.to_csv('new_headlines_labels.csv', mode='a', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>headline</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Trudeau announces $62.5 million for seafood, f...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coronavirus: Disinfectant riff the latest of T...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coronavirus: What's happening in Canada and ar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Is it safe to go running during the coronaviru...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coronavirus: Brazil emerging as world hot spot...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Disinfectant Against Coronavirus: How to Make ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Is the curve flattening? Expert says coronavir...</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>'We knew that the foreign workers would be a s...</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Virginia sees 772 new coronavirus cases, now r...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Canada could see 22,000 coronavirus deaths eve...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             headline  label\n",
       "0   Trudeau announces $62.5 million for seafood, f...      0\n",
       "1   Coronavirus: Disinfectant riff the latest of T...      0\n",
       "2   Coronavirus: What's happening in Canada and ar...      0\n",
       "3   Is it safe to go running during the coronaviru...      1\n",
       "4   Coronavirus: Brazil emerging as world hot spot...      0\n",
       "..                                                ...    ...\n",
       "95  Disinfectant Against Coronavirus: How to Make ...      0\n",
       "96  Is the curve flattening? Expert says coronavir...     -1\n",
       "97  'We knew that the foreign workers would be a s...     -1\n",
       "98  Virginia sees 772 new coronavirus cases, now r...      0\n",
       "99  Canada could see 22,000 coronavirus deaths eve...      0\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive headlines:\n",
      "\n",
      "['Is it safe to go running during the coronavirus pandemic?',\n",
      " \"Coronavirus: Belarus orphanage seeks help amid 'critical' outbreak\",\n",
      " 'Mandatory Face Masks And More: Canadian Airlines Introduce New Coronavirus Travel Safety Measures',\n",
      " 'Running is enjoying a boom because of the coronavirus pandemic',\n",
      " 'Coronavirus antibody testing a big deal? What to know about the promising serology tests']\n",
      "\n",
      "Negative headlines:\n",
      "\n",
      "['Coronavirus: Asian-Americans turn to guns, GoPros to deter attacks',\n",
      " 'Somali civilian shot dead by police amid coronavirus restrictions',\n",
      " \"'They are trying to steal everything.' US coronavirus response hit by foreign hackers\",\n",
      " 'After her husband died from coronavirus, she found an emotional goodbye note on his phone',\n",
      " \"Singapore's migrant workers are suffering the brunt of the country's coronavirus outbreak\"]\n"
     ]
    }
   ],
   "source": [
    "print(\"Positive headlines:\\n\")\n",
    "pprint(list(df2[df2['label'] == 1].headline)[:5], width=200)\n",
    "\n",
    "print(\"\\nNegative headlines:\\n\")\n",
    "pprint(list(df2[df2['label'] == -1].headline)[:5], width=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0    60\n",
      "-1    26\n",
      " 1    14\n",
      "Name: label, dtype: int64\n",
      " 0    60.0\n",
      "-1    26.0\n",
      " 1    14.0\n",
      "Name: label, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df2.label.value_counts())\n",
    "\n",
    "print(df2.label.value_counts(normalize=True) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "counts = df2.label.value_counts(normalize=True) * 100\n",
    "\n",
    "sns.barplot(x=counts.index, y=counts, ax=ax)\n",
    "\n",
    "ax.set_xticklabels(['Negative', 'Neutral', 'Positive'])\n",
    "ax.set_ylabel(\"Percentage\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenizers and Stopwords "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['This', 'is', 'an', 'example', 'sentence', '!', 'However', ',', 'it', 'is', \"n't\", 'a', 'very', 'informative', 'one']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /Users/sahil/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import word_tokenize, RegexpTokenizer\n",
    "nltk.downloader.download('punkt')\n",
    "\n",
    "example = \"This is an example sentence! However, it isn't a very informative one\"\n",
    "\n",
    "print(word_tokenize(example, language='english'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This',\n",
       " 'is',\n",
       " 'an',\n",
       " 'example',\n",
       " 'sentence',\n",
       " 'However',\n",
       " 'it',\n",
       " 'isn',\n",
       " 't',\n",
       " 'a',\n",
       " 'very',\n",
       " 'informative',\n",
       " 'one']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "tokenizer.tokenize(example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /Users/sahil/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "nltk.downloader.download('stopwords')\n",
    "stop_words = stopwords.words('english')\n",
    "print(stop_words[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Distributions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_text(headlines):\n",
    "    tokens = []\n",
    "    for line in headlines:\n",
    "        toks = tokenizer.tokenize(line)\n",
    "        toks = [t.lower() for t in toks if t.lower() not in stop_words]\n",
    "        tokens.extend(toks)\n",
    "    \n",
    "    return tokens\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokens = process_text(titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('coronavirus', 14),\n",
       " ('pandemic', 4),\n",
       " ('running', 2),\n",
       " ('workers', 2),\n",
       " ('bill', 2),\n",
       " ('open', 2),\n",
       " ('data', 2),\n",
       " ('safe', 1),\n",
       " ('go', 1),\n",
       " ('belarus', 1),\n",
       " ('orphanage', 1),\n",
       " ('seeks', 1),\n",
       " ('help', 1),\n",
       " ('amid', 1),\n",
       " ('critical', 1),\n",
       " ('outbreak', 1),\n",
       " ('mandatory', 1),\n",
       " ('face', 1),\n",
       " ('masks', 1),\n",
       " ('canadian', 1)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_lines = list(df2[df2.label == 1].headline)\n",
    "\n",
    "pos_tokens = process_text(pos_lines)\n",
    "pos_freq = nltk.FreqDist(pos_tokens)\n",
    "\n",
    "pos_freq.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_val = [x[1] for x in pos_freq.most_common()]\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.plot(y_val)\n",
    "\n",
    "plt.xlabel(\"Words\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Word Frequency Distribution (Positive)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_final = []\n",
    "for i, k, z, t in zip(y_val[0::4], y_val[1::4], y_val[2::4], y_val[3::4]):\n",
    "    y_final.append(math.log(i + k + z + t))\n",
    "\n",
    "x_val = [math.log(i + 1) for i in range(len(y_final))]\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.xlabel(\"Words (Log)\")\n",
    "plt.ylabel(\"Frequency (Log)\")\n",
    "plt.title(\"Word Frequency Distribution (Positive)\")\n",
    "plt.plot(x_val, y_final)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('coronavirus', 25),\n",
       " ('us', 7),\n",
       " ('death', 4),\n",
       " ('response', 2),\n",
       " ('foreign', 2),\n",
       " ('died', 2),\n",
       " ('singapore', 2),\n",
       " ('workers', 2),\n",
       " ('researchers', 2),\n",
       " ('chloroquine', 2),\n",
       " ('study', 2),\n",
       " ('may', 2),\n",
       " ('asian', 1),\n",
       " ('americans', 1),\n",
       " ('turn', 1),\n",
       " ('guns', 1),\n",
       " ('gopros', 1),\n",
       " ('deter', 1),\n",
       " ('attacks', 1),\n",
       " ('somali', 1)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_lines = list(df2[df2.label == -1].headline)\n",
    "\n",
    "neg_tokens = process_text(neg_lines)\n",
    "neg_freq = nltk.FreqDist(neg_tokens)\n",
    "\n",
    "neg_freq.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_val = [x[1] for x in neg_freq.most_common()]\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.plot(y_val)\n",
    "\n",
    "plt.xlabel(\"Words\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Word Frequency Distribution (Negative)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_final = []\n",
    "for i, k, z in zip(y_val[0::3], y_val[1::3], y_val[2::3]):\n",
    "    if i + k + z == 0:\n",
    "        break\n",
    "    y_final.append(math.log(i + k + z))\n",
    "\n",
    "x_val = [math.log(i+1) for i in range(len(y_final))]\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.xlabel(\"Words (Log)\")\n",
    "plt.ylabel(\"Frequency (Log)\")\n",
    "plt.title(\"Word Frequency Distribution (Negative)\")\n",
    "plt.plot(x_val, y_final)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FUTURE SCOPE: \n",
    "WE CAN USE CONTENT OF THE NEWS FOR MORE ACCURATE RESUTS."
   ]
  },
  {
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}