mainFile.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "from tqdm import tqdm \n",
    "from textblob import TextBlob\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Required Links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "links=['YouTubeRewind 2015.html',\n",
    "      'YouTubeRewind 2016.html',\n",
    "      'YouTubeRewind 2019.html']\n",
    "\n",
    "videoId=['KK9bwTlAvgo','_GuOjXYl5ew','2lAe1cqCOXo']\n",
    "videoId=['Rewind 2015','Rewind 2016','Rewind 2019']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertToNumber(txt):\n",
    "    numbers=re.findall(r'\\d+', txt)\n",
    "    return int(''.join(numbers[ : ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateData(soup):\n",
    "    \n",
    "    #series to store data\n",
    "    videoData=pd.Series(dtype='object')\n",
    "    \n",
    "    # \"channel name\" is in HTML tag whose class name is \"ytd-channel-name\"\n",
    "    videoData['channelName']=soup.find('yt-formatted-string',class_='ytd-channel-name').text\n",
    "    \n",
    "    # \"video title\" is in H1 HTML tag whose class name is \"title\"\n",
    "    videoData['title']=soup.find('h1',class_='title').text\n",
    "    \n",
    "    # \"video length\" is in HTML tag whose class name is \"ytp-bound-time-right\"\n",
    "    videoData['videoLength']=soup.find(class_='ytp-bound-time-right').text\n",
    "\n",
    "    # Total \"views\" of a video is in \"<span>\" whose class name is \"view-count\"\n",
    "    views=soup.find('span',class_='view-count').text # return like \"15,113 views\"\n",
    "    videoData['views']=convertToNumber(views) # this will convert str into numeric i.e 15113\n",
    "\n",
    "    # Total \"likes\" of a video is in \"aria-label\" attribute of an 2nd(or 1 index value) HTML tag\n",
    "    likes=soup.find_all(class_='style-scope ytd-toggle-button-renderer style-text')[1]['aria-label']\n",
    "    videoData['likes']=convertToNumber(likes)\n",
    "\n",
    "    # Total \"dislikes\" of a video is in \"aria-label\" attribute of an 2nd(or 3 index value) HTML tag\n",
    "    dislikes=soup.find_all(class_='style-scope ytd-toggle-button-renderer style-text')[3]['aria-label']\n",
    "    videoData['dislikes']=convertToNumber(dislikes)\n",
    "    \n",
    "    #total \"comments\" of video is in <div id='contents'>\n",
    "    allComments=soup.find_all('div',id='contents')\n",
    "    cmts=allComments[0].find_all(id='author-text')\n",
    "    \n",
    "    lowerLimit=1 \n",
    "    upperLimit=201 #total comment I want to extract from a video\n",
    "\n",
    "    users=[] \n",
    "    comments=[]\n",
    "\n",
    "    #loop to extract each comment and tqdm is used to show progress bars\n",
    "    for i in tqdm(range(lowerLimit,upperLimit)):\n",
    "        userName=allComments[0].find_all(id='author-text')[i].text.strip()\n",
    "        comment=allComments[0].find_all(id='content-text')[i].text.strip()\n",
    "\n",
    "        users.append(userName)\n",
    "        comments.append(comment.strip())\n",
    "    videoData['users']=np.array(users)\n",
    "    videoData['comments']=np.array(comments)\n",
    "    return videoData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this function will generate the dataframe of extracted data into rows and columns\n",
    "def generateDataframe(videoData):\n",
    "    df=pd.DataFrame()\n",
    "    df['username']=videoData['users']\n",
    "    df['comment']=videoData['comments']\n",
    "    df['videoid']=videoData['videoid']\n",
    "    df['title']=videoData['title']\n",
    "    df['views']=videoData['views']\n",
    "    df['likes']=videoData['likes']\n",
    "    df['dislikes']=videoData['dislikes']\n",
    "    return df"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The below function is used to generate polarities (i.e a comment is positive, neutral or negative)\n",
    "of comments that are extracted from a video and\n",
    "\"TextBolb\" pre-defined library is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generatepolarity(df):\n",
    "    polarity=[]\n",
    "    \n",
    "    #loop to go through each comment and get polarity\n",
    "    for i in df['comment']:\n",
    "        c1=TextBlob(str(i))\n",
    "        polarity.append(c1.sentiment[0])\n",
    "    df['polarity']=polarity    \n",
    "\n",
    "    #labeling polarity\n",
    "    label=[]\n",
    "    for i in df['polarity']:\n",
    "        if(i<0):\n",
    "            label.append('negative')#negative\n",
    "        elif(i==0):\n",
    "            label.append('neutral')#neutral\n",
    "        elif(i>0):\n",
    "            label.append('positive')#positive\n",
    "    df['label']=label\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "If the webpage is static use this method\n",
    "URL = 'https://www.youtube.com/watch?v=hxRJTjeDvuY'\n",
    "page = requests.get(URL)\n",
    "soup = BeautifulSoup(page.content, 'lxml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:47<00:00,  4.22it/s]\n",
      "100%|██████████| 200/200 [00:41<00:00,  4.88it/s]\n",
      "100%|██████████| 200/200 [00:47<00:00,  4.20it/s]\n"
     ]
    }
   ],
   "source": [
    "count=1\n",
    "#iterate to each HTML page/video page\n",
    "for ids,link in zip(videoId,links):\n",
    "    \n",
    "    #pulling data out of HTML\n",
    "    soup = BeautifulSoup(open(link,'r+',encoding='utf8').read())\n",
    "    \n",
    "    videoData=generateData(soup)\n",
    "    videoData['videoid']=ids\n",
    "    df=generateDataframe(videoData)\n",
    "    df=generatepolarity(df)\n",
    "    \n",
    "    #creating dataframe for each video\n",
    "    fn='myDataDf'+str(count)+'.csv'\n",
    "    df.to_csv(fn,index=False)\n",
    "    count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reindex(columns=['videoid', 'title','username', 'comment' , 'views', 'likes', 'dislikes',\n",
    "       'polarity', 'label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>username</th>\n",
       "      <th>comment</th>\n",
       "      <th>videoid</th>\n",
       "      <th>title</th>\n",
       "      <th>views</th>\n",
       "      <th>likes</th>\n",
       "      <th>dislikes</th>\n",
       "      <th>polarity</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>Joebob Derby</td>\n",
       "      <td>Rewind 2020: “top 10 demonetized videos”</td>\n",
       "      <td>Rewind 2019</td>\n",
       "      <td>YouTube Rewind 2019: For the Record | #YouTube...</td>\n",
       "      <td>98205677</td>\n",
       "      <td>3332526</td>\n",
       "      <td>8749993</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Bob Smith</td>\n",
       "      <td>I've been watching every rewind since 2012, co...</td>\n",
       "      <td>Rewind 2015</td>\n",
       "      <td>YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...</td>\n",
       "      <td>151641936</td>\n",
       "      <td>2899029</td>\n",
       "      <td>208570</td>\n",
       "      <td>-0.750000</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>JIVIN J</td>\n",
       "      <td>2015 rewind-perfection by Youtube \\n2019 rewin...</td>\n",
       "      <td>Rewind 2015</td>\n",
       "      <td>YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...</td>\n",
       "      <td>151641936</td>\n",
       "      <td>2899029</td>\n",
       "      <td>208570</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>Scott Arcement</td>\n",
       "      <td>2016: This is awful\\n2019: This is fine</td>\n",
       "      <td>Rewind 2016</td>\n",
       "      <td>YouTube Rewind: The Ultimate 2016 Challenge | ...</td>\n",
       "      <td>236654813</td>\n",
       "      <td>3904159</td>\n",
       "      <td>563822</td>\n",
       "      <td>-0.291667</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>Quin Roseman</td>\n",
       "      <td>this is my worst nightmare of a comment section</td>\n",
       "      <td>Rewind 2019</td>\n",
       "      <td>YouTube Rewind 2019: For the Record | #YouTube...</td>\n",
       "      <td>98205677</td>\n",
       "      <td>3332526</td>\n",
       "      <td>8749993</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>DaSpammer58</td>\n",
       "      <td>Rewind 2015: let’s have a party bus!\\nRewind 2...</td>\n",
       "      <td>Rewind 2015</td>\n",
       "      <td>YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...</td>\n",
       "      <td>151641936</td>\n",
       "      <td>2899029</td>\n",
       "      <td>208570</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>ArySilvi 98</td>\n",
       "      <td>Who remember this beautiful moments..?</td>\n",
       "      <td>Rewind 2016</td>\n",
       "      <td>YouTube Rewind: The Ultimate 2016 Challenge | ...</td>\n",
       "      <td>236654813</td>\n",
       "      <td>3904159</td>\n",
       "      <td>563822</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>TomPaulsen21 -</td>\n",
       "      <td>YouTube last year: the most disliked video.\\nY...</td>\n",
       "      <td>Rewind 2019</td>\n",
       "      <td>YouTube Rewind 2019: For the Record | #YouTube...</td>\n",
       "      <td>98205677</td>\n",
       "      <td>3332526</td>\n",
       "      <td>8749993</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>Yaboy Player</td>\n",
       "      <td>Me: watches a quarter of the video\\n\\nMe: stop...</td>\n",
       "      <td>Rewind 2019</td>\n",
       "      <td>YouTube Rewind 2019: For the Record | #YouTube...</td>\n",
       "      <td>98205677</td>\n",
       "      <td>3332526</td>\n",
       "      <td>8749993</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Double A Food Reveiw</td>\n",
       "      <td>Remember when you had an english project due t...</td>\n",
       "      <td>Rewind 2019</td>\n",
       "      <td>YouTube Rewind 2019: For the Record | #YouTube...</td>\n",
       "      <td>98205677</td>\n",
       "      <td>3332526</td>\n",
       "      <td>8749993</td>\n",
       "      <td>0.141667</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 username                                            comment  \\\n",
       "548          Joebob Derby           Rewind 2020: “top 10 demonetized videos”   \n",
       "63              Bob Smith  I've been watching every rewind since 2012, co...   \n",
       "95                JIVIN J  2015 rewind-perfection by Youtube \\n2019 rewin...   \n",
       "367        Scott Arcement            2016: This is awful\\n2019: This is fine   \n",
       "565          Quin Roseman    this is my worst nightmare of a comment section   \n",
       "169           DaSpammer58  Rewind 2015: let’s have a party bus!\\nRewind 2...   \n",
       "327           ArySilvi 98             Who remember this beautiful moments..?   \n",
       "489        TomPaulsen21 -  YouTube last year: the most disliked video.\\nY...   \n",
       "468          Yaboy Player  Me: watches a quarter of the video\\n\\nMe: stop...   \n",
       "410  Double A Food Reveiw  Remember when you had an english project due t...   \n",
       "\n",
       "         videoid                                              title  \\\n",
       "548  Rewind 2019  YouTube Rewind 2019: For the Record | #YouTube...   \n",
       "63   Rewind 2015  YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...   \n",
       "95   Rewind 2015  YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...   \n",
       "367  Rewind 2016  YouTube Rewind: The Ultimate 2016 Challenge | ...   \n",
       "565  Rewind 2019  YouTube Rewind 2019: For the Record | #YouTube...   \n",
       "169  Rewind 2015  YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...   \n",
       "327  Rewind 2016  YouTube Rewind: The Ultimate 2016 Challenge | ...   \n",
       "489  Rewind 2019  YouTube Rewind 2019: For the Record | #YouTube...   \n",
       "468  Rewind 2019  YouTube Rewind 2019: For the Record | #YouTube...   \n",
       "410  Rewind 2019  YouTube Rewind 2019: For the Record | #YouTube...   \n",
       "\n",
       "         views    likes  dislikes  polarity     label  \n",
       "548   98205677  3332526   8749993  0.500000  positive  \n",
       "63   151641936  2899029    208570 -0.750000  negative  \n",
       "95   151641936  2899029    208570  0.000000   neutral  \n",
       "367  236654813  3904159    563822 -0.291667  negative  \n",
       "565   98205677  3332526   8749993 -1.000000  negative  \n",
       "169  151641936  2899029    208570  0.000000   neutral  \n",
       "327  236654813  3904159    563822  0.850000  positive  \n",
       "489   98205677  3332526   8749993  0.200000  positive  \n",
       "468   98205677  3332526   8749993  0.000000   neutral  \n",
       "410   98205677  3332526   8749993  0.141667  positive  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=pd.concat([pd.read_csv('myDataDf1.csv'),pd.read_csv('myDataDf2.csv'),pd.read_csv('myDataDf3.csv')],ignore_index=True)\n",
    "df4.to_csv('myDataDf.csv',index=False)\n",
    "df4.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>username</th>\n",
       "      <th>comment</th>\n",
       "      <th>videoid</th>\n",
       "      <th>title</th>\n",
       "      <th>views</th>\n",
       "      <th>likes</th>\n",
       "      <th>dislikes</th>\n",
       "      <th>polarity</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maisie Mears</td>\n",
       "      <td>Only here because 2019 wasn’t even a rewind ffs</td>\n",
       "      <td>Rewind 2015</td>\n",
       "      <td>YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...</td>\n",
       "      <td>151641936</td>\n",
       "      <td>2899029</td>\n",
       "      <td>208570</td>\n",
       "      <td>0.00</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iambirb</td>\n",
       "      <td>5 years later and I just now realize the dance...</td>\n",
       "      <td>Rewind 2015</td>\n",
       "      <td>YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...</td>\n",
       "      <td>151641936</td>\n",
       "      <td>2899029</td>\n",
       "      <td>208570</td>\n",
       "      <td>0.00</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>juliaa</td>\n",
       "      <td>this made me so nostalgic i felt like crying. ...</td>\n",
       "      <td>Rewind 2015</td>\n",
       "      <td>YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...</td>\n",
       "      <td>151641936</td>\n",
       "      <td>2899029</td>\n",
       "      <td>208570</td>\n",
       "      <td>0.10</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Erf Boy</td>\n",
       "      <td>Only people who hated Rewind 2019 can like thi...</td>\n",
       "      <td>Rewind 2015</td>\n",
       "      <td>YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...</td>\n",
       "      <td>151641936</td>\n",
       "      <td>2899029</td>\n",
       "      <td>208570</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohit</td>\n",
       "      <td>who else is here after the terrible 2018 rewind?</td>\n",
       "      <td>Rewind 2015</td>\n",
       "      <td>YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...</td>\n",
       "      <td>151641936</td>\n",
       "      <td>2899029</td>\n",
       "      <td>208570</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       username                                            comment  \\\n",
       "0  Maisie Mears    Only here because 2019 wasn’t even a rewind ffs   \n",
       "1       iambirb  5 years later and I just now realize the dance...   \n",
       "2        juliaa  this made me so nostalgic i felt like crying. ...   \n",
       "3       Erf Boy  Only people who hated Rewind 2019 can like thi...   \n",
       "4         Mohit   who else is here after the terrible 2018 rewind?   \n",
       "\n",
       "       videoid                                              title      views  \\\n",
       "0  Rewind 2015  YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...  151641936   \n",
       "1  Rewind 2015  YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...  151641936   \n",
       "2  Rewind 2015  YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...  151641936   \n",
       "3  Rewind 2015  YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...  151641936   \n",
       "4  Rewind 2015  YouTube Rewind: Now Watch Me 2015 | #YouTubeRe...  151641936   \n",
       "\n",
       "     likes  dislikes  polarity     label  \n",
       "0  2899029    208570      0.00   neutral  \n",
       "1  2899029    208570      0.00   neutral  \n",
       "2  2899029    208570      0.10  positive  \n",
       "3  2899029    208570     -0.45  negative  \n",
       "4  2899029    208570     -1.00  negative  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('myDataDf.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Analysing Polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>label</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>positive</th>\n",
       "      <th>total</th>\n",
       "      <th>posFraction</th>\n",
       "      <th>negFraction</th>\n",
       "      <th>neuFraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>videoid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rewind 2015</th>\n",
       "      <td>32</td>\n",
       "      <td>73</td>\n",
       "      <td>95</td>\n",
       "      <td>200</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rewind 2016</th>\n",
       "      <td>32</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>200</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rewind 2019</th>\n",
       "      <td>46</td>\n",
       "      <td>74</td>\n",
       "      <td>80</td>\n",
       "      <td>200</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "label        negative  neutral  positive  total  posFraction  negFraction  \\\n",
       "videoid                                                                     \n",
       "Rewind 2015        32       73        95    200        0.475         0.16   \n",
       "Rewind 2016        32       87        81    200        0.405         0.16   \n",
       "Rewind 2019        46       74        80    200        0.400         0.23   \n",
       "\n",
       "label        neuFraction  \n",
       "videoid                   \n",
       "Rewind 2015        0.365  \n",
       "Rewind 2016        0.435  \n",
       "Rewind 2019        0.370  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.crosstab(df['videoid'],df['label'])\n",
    "df2['total']=df2.sum(axis=1)\n",
    "df2['posFraction']=df2['positive']/df2['total']\n",
    "df2['negFraction']=df2['negative']/df2['total']\n",
    "df2['neuFraction']=df2['neutral']/df2['total']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(ncols=3)\n",
    "fig.set_size_inches(15,5)\n",
    "sns.barplot(df.label.unique(),df2[df.label.unique()].loc['Rewind 2015'],ax=axs[0])\n",
    "sns.barplot(df.label.unique(),df2[df.label.unique()].loc['Rewind 2016'],ax=axs[1])\n",
    "sns.barplot(df.label.unique(),df2[df.label.unique()].loc['Rewind 2019'],ax=axs[2])\n",
    "\n",
    "for i in range(len(df2[df.label.unique()].index)):\n",
    "    col=df2[df.label.unique()].index[i]\n",
    "    for x, y in enumerate(df2[df.label.unique()].loc[col]):\n",
    "        axs[i].text(x, y, str(y), ha=\"center\")\n",
    "    axs[i].set_ylabel(\"Comments\")\n",
    "    axs[i].set_title(col)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df2.index, df2.posFraction, color='Green');\n",
    "plt.bar(df2.index, df2.neuFraction, bottom=df2.posFraction, color='lightblue');\n",
    "plt.bar(df2.index, df2.negFraction, bottom=df2.posFraction + df2.neuFraction, color='Red');\n",
    "plt.legend(df2.columns,bbox_to_anchor=(0.3, 0.9, 1., .102))\n",
    "plt.show();"
   ]
  },
  {
   "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>label</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>videoid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rewind 2015</th>\n",
       "      <td>32</td>\n",
       "      <td>73</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rewind 2016</th>\n",
       "      <td>32</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rewind 2019</th>\n",
       "      <td>46</td>\n",
       "      <td>74</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "label        negative  neutral  positive\n",
       "videoid                                 \n",
       "Rewind 2015        32       73        95\n",
       "Rewind 2016        32       87        81\n",
       "Rewind 2019        46       74        80"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[['negative','neutral','positive']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.get_cmap('tab10')\n",
    "outer_colors = cmap(np.array([0, 1, 4]))\n",
    "inner_colors = cmap(np.array([3, 9, 2, 3, 9, 2, 3, 9, 2]))\n",
    "\n",
    "plt.pie(df2.total, labels=df2.index, wedgeprops=dict(width=0.3),colors=outer_colors);\n",
    "p1 =plt.pie(df2[['negative','neutral','positive']].values.flatten(),\n",
    "                                  labels = df2[['negative','neutral','positive']].values.flatten(),\n",
    "                                  labeldistance=0.7 ,rotatelabels=True,radius=0.7, wedgeprops=dict(width=0.3),\n",
    "                                  colors=inner_colors,frame=True);\n",
    "plt.legend(p1[0], ['negative','neutral','positive'],title=\"Polarities Class\",loc=\"center left\",bbox_to_anchor=(1, 0, 0.5, 1))\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Analysing Views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2=df[['videoid','views']].groupby('videoid').mean()\n",
    "\n",
    "sns.barplot(df2.index, df2.views, color='blue',palette=\"Blues_d\");\n",
    "plt.xlabel('Video Title')\n",
    "plt.ylabel('Views')\n",
    "for x, val in enumerate(df2.index):\n",
    "    y = df2.loc[val, 'views']+1000000\n",
    "    plt.text(x, y, str(y), ha=\"center\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Analysing Likes and Dislikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>likes</th>\n",
       "      <th>dislikes</th>\n",
       "      <th>total</th>\n",
       "      <th>likesFraction</th>\n",
       "      <th>dislikesFraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>videoid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rewind 2015</th>\n",
       "      <td>2899029</td>\n",
       "      <td>208570</td>\n",
       "      <td>3107599</td>\n",
       "      <td>0.932884</td>\n",
       "      <td>0.067116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rewind 2016</th>\n",
       "      <td>3904159</td>\n",
       "      <td>563822</td>\n",
       "      <td>4467981</td>\n",
       "      <td>0.873808</td>\n",
       "      <td>0.126192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rewind 2019</th>\n",
       "      <td>3332526</td>\n",
       "      <td>8749993</td>\n",
       "      <td>12082519</td>\n",
       "      <td>0.275814</td>\n",
       "      <td>0.724186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               likes  dislikes     total  likesFraction  dislikesFraction\n",
       "videoid                                                                  \n",
       "Rewind 2015  2899029    208570   3107599       0.932884          0.067116\n",
       "Rewind 2016  3904159    563822   4467981       0.873808          0.126192\n",
       "Rewind 2019  3332526   8749993  12082519       0.275814          0.724186"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df[['videoid','likes','dislikes']].groupby('videoid').mean()\n",
    "df2['total']=df2.sum(axis=1)\n",
    "df2['likesFraction']=df2['likes']/df2['total']\n",
    "df2['dislikesFraction']=df2['dislikes']/df2['total']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(ncols=2)\n",
    "fig.set_size_inches(10,5)\n",
    "sns.barplot(df2.index, df2.likes, color='Green',ax=axs[0]);\n",
    "sns.barplot(df2.index, df2.dislikes, color='red',ax=axs[1]);\n",
    "for x, val in enumerate(df2.index):\n",
    "    y1 = df2.loc[val, 'likes']+1000\n",
    "    y2 = df2.loc[val, 'dislikes']+1000\n",
    "    axs[0].text(x, y1, str(y1), ha=\"center\");\n",
    "    axs[1].text(x, y2, str(y2), ha=\"center\");\n",
    "    axs[0].set_title(\"Figure 1\")\n",
    "    axs[1].set_title(\"Figure 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.gcf();\n",
    "fig.set_size_inches(6, 6);\n",
    "sns.barplot(df2.index, df2.likes, color='Green',label='Likes');\n",
    "sns.barplot(df2.index, df2.dislikes, bottom=df2.likes, color='red',label='Dislikes');\n",
    "plt.ylabel(\"Likes/Dislikes\")\n",
    "plt.xlabel(\"Video Title\")\n",
    "plt.title(\"Figure 3\")\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.gcf();\n",
    "fig.set_size_inches(6, 6);\n",
    "p1=sns.barplot(df2.index, df2.likesFraction, color='Green',label='Likes');\n",
    "p2=sns.barplot(df2.index, df2.dislikesFraction, bottom=df2.likesFraction, color='red',label='Dislikes');\n",
    "plt.ylabel(\"Likes/Dislikes\")\n",
    "plt.xlabel(\"Video Title\")\n",
    "plt.legend(bbox_to_anchor=(0.25, 0.915, 1., .102))\n",
    "plt.title(\"Figure 4\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.3 64-bit ('base': conda)",
   "language": "python",
   "name": "python38364bitbaseconda14573fd2ccc84d04813e3b8f229a0662"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}