youtube_trending/youtube_sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install sqlalchemy-singlestoredb singlestoredb sqlalchemy pandas matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "import pandas as pd\n",
    "\n",
    "eng = create_engine('singlestoredb://root:test@172.17.0.3/test?local_infile=True')\n",
    "conn = eng.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>view_count</th>\n",
       "      <th>likes</th>\n",
       "      <th>dislikes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I Haven't Been Honest About My Injury.. Here's...</td>\n",
       "      <td>949491</td>\n",
       "      <td>77487</td>\n",
       "      <td>746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SURPRISING MY DAD WITH HIS DREAM TRUCK!! | Lou...</td>\n",
       "      <td>1402687</td>\n",
       "      <td>95694</td>\n",
       "      <td>2158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Try Not To Laugh Challenge #51</td>\n",
       "      <td>591837</td>\n",
       "      <td>44168</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ten Minutes with Tyler Cameron | Q&amp;A</td>\n",
       "      <td>105955</td>\n",
       "      <td>4511</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cardi B - WAP feat. Megan Thee Stallion [Offic...</td>\n",
       "      <td>76805026</td>\n",
       "      <td>2820345</td>\n",
       "      <td>382578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  view_count    likes  \\\n",
       "0  I Haven't Been Honest About My Injury.. Here's...      949491    77487   \n",
       "1  SURPRISING MY DAD WITH HIS DREAM TRUCK!! | Lou...     1402687    95694   \n",
       "2                     Try Not To Laugh Challenge #51      591837    44168   \n",
       "3               Ten Minutes with Tyler Cameron | Q&A      105955     4511   \n",
       "4  Cardi B - WAP feat. Megan Thee Stallion [Offic...    76805026  2820345   \n",
       "\n",
       "   dislikes  \n",
       "0       746  \n",
       "1      2158  \n",
       "2       409  \n",
       "3        69  \n",
       "4    382578  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('SELECT title, view_count, likes, dislikes FROM youtube limit 5', conn)"
   ]
  },
  {
   "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></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count(*)</th>\n",
       "      <td>9621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max(likes)</th>\n",
       "      <td>7110071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg(likes)</th>\n",
       "      <td>98988.9306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median(likes)</th>\n",
       "      <td>35492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max(view_count)</th>\n",
       "      <td>76805026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg(view_count)</th>\n",
       "      <td>1312018.6248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median(view_count)</th>\n",
       "      <td>641518.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               0\n",
       "count(*)                    9621\n",
       "max(likes)               7110071\n",
       "avg(likes)            98988.9306\n",
       "median(likes)            35492.0\n",
       "max(view_count)         76805026\n",
       "avg(view_count)     1312018.6248\n",
       "median(view_count)      641518.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at some basic video statistics\n",
    "pd.read_sql_query(\"\"\"\n",
    "    select\n",
    "        count(*),\n",
    "        max(likes), avg(likes), median(likes),\n",
    "        max(view_count), avg(view_count), median(view_count)\n",
    "    from youtube\n",
    "\"\"\", conn).astype(str).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "wasm_b64 = base64.b64encode(open(\"../target/wasm32-wasi/release/rust_wasm_gs_demo.wasm\", \"rb\").read())\n",
    "wit_b64 = base64.b64encode(open(\"../sentiment.wit\", \"rb\").read())\n",
    "\n",
    "conn = eng.connect()\n",
    "# load the sentiment Wasm functions into SingleStore\n",
    "conn.execute(\"\"\"\n",
    "    create or replace function sentiment as wasm\n",
    "    from base64 :1\n",
    "    with wit from base64 :2;\n",
    "\"\"\", wasm_b64, wit_b64).close()\n",
    "\n",
    "conn.execute(\"\"\"\n",
    "    create or replace function sentiment_t returns table as wasm\n",
    "    from base64 :1\n",
    "    with wit from base64 :2;\n",
    "\"\"\", wasm_b64, wit_b64).close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.380187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment\n",
       "0   0.380187"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we have a function called sentiment which can perform sentiment\n",
    "# analysis on a text like so:\n",
    "pd.read_sql_query(\"\"\"\n",
    "    select sentiment('SingleStore is cool!') as sentiment\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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_sentiment</th>\n",
       "      <th>description_sentiment</th>\n",
       "      <th>tags_sentiment</th>\n",
       "      <th>num_rows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009774</td>\n",
       "      <td>0.38485</td>\n",
       "      <td>0.034376</td>\n",
       "      <td>9621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   title_sentiment  description_sentiment  tags_sentiment  num_rows\n",
       "0         0.009774                0.38485        0.034376      9621"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the avg sentiment of all the videos\n",
    "\n",
    "pd.read_sql_query(f\"\"\"\n",
    "    select\n",
    "        avg(sentiment(title)) as title_sentiment,\n",
    "        avg(sentiment(video_description)) as description_sentiment,\n",
    "        avg(sentiment(tags)) as tags_sentiment,\n",
    "        count(*) as num_rows\n",
    "    from youtube\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='bucket'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare the average sentiment (y-axis) to the number of comments (x-axis)\n",
    "\n",
    "pd.read_sql_query(f\"\"\"\n",
    "    select bucket, avg(score) as sentiment\n",
    "    from (\n",
    "        select *, pow(2, floor(log(2, comment_count))) as bucket, table_col as score\n",
    "        from youtube, sentiment_t(title)\n",
    "    )\n",
    "    group by bucket order by bucket asc\n",
    "\"\"\", conn).plot(x='bucket', y=['sentiment'], kind='line', figsize=(10, 2), logx=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='bucket'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare the average sentiment (y-axis) to the number of likes (x-axis)\n",
    "\n",
    "pd.read_sql_query(f\"\"\"\n",
    "    select bucket, avg(score) as sentiment\n",
    "    from (\n",
    "        select *, pow(2, floor(log(2, likes))) as bucket, table_col as score\n",
    "        from youtube, sentiment_t(title)\n",
    "    )\n",
    "    group by bucket order by bucket asc\n",
    "\"\"\", conn).plot(x='bucket', y=['sentiment'], kind='line', figsize=(10, 2), logx=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# is there any correlation between trending month and sentiment?\n",
    "df = pd.read_sql_query(f\"\"\"\n",
    "    select\n",
    "        date_format(trending_date, \"%b %y\") as date,\n",
    "        avg(table_col) as title_sentiment\n",
    "    from youtube, sentiment_t(title)\n",
    "    group by 1\n",
    "    order by trending_date asc\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='date'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.query(\"date.notnull()\").plot(x='date', y=['title_sentiment'], kind='bar', figsize=(10, 3), rot=0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.15 64-bit",
   "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",
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