audible_eda/audible_reviews_scraper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# import nest_asyncio\n",
    "# nest_asyncio.apply()\n",
    "from requests_html import HTML, HTMLSession, AsyncHTMLSession\n",
    "from threading import Thread\n",
    "import time\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\Toby-PC\\Documents\\code\\audible\\all_english_audible.csv')\n",
    "df = df[~df['asin'].isnull()]\n",
    "\n",
    "image_path = r'C:\\Users\\Toby-PC\\Documents\\code\\audible\\figures'\n",
    "\n",
    "def save_fig(fig_name, tight_layout=True):\n",
    "    path = os.path.join(image_path, fig_name + '.png')\n",
    "    print(\"Saving figure\", fig_name)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64023215.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_ratings = df['rating_count'].sum()\n",
    "total_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values('rating_count', ascending=False)\n",
    "df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "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>index</th>\n",
       "      <th>asin</th>\n",
       "      <th>author</th>\n",
       "      <th>category</th>\n",
       "      <th>length</th>\n",
       "      <th>link</th>\n",
       "      <th>narrator</th>\n",
       "      <th>price</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>247928</td>\n",
       "      <td>B005FRGT44</td>\n",
       "      <td>Ernest Cline</td>\n",
       "      <td>Sci-Fi &amp; Fantasy</td>\n",
       "      <td>15 hrs and 40 mins</td>\n",
       "      <td>https://www.audible.com/pd/Ready-Player-One-Au...</td>\n",
       "      <td>Wil Wheaton</td>\n",
       "      <td>31.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>216094.0</td>\n",
       "      <td>2011-08-16</td>\n",
       "      <td>Ready Player One</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>248009</td>\n",
       "      <td>B00B5HZGUG</td>\n",
       "      <td>Andy Weir</td>\n",
       "      <td>Sci-Fi &amp; Fantasy</td>\n",
       "      <td>10 hrs and 53 mins</td>\n",
       "      <td>https://www.audible.com/pd/The-Martian-Audiobo...</td>\n",
       "      <td>R. C. Bray</td>\n",
       "      <td>29.99</td>\n",
       "      <td>5.0</td>\n",
       "      <td>164988.0</td>\n",
       "      <td>2013-03-22</td>\n",
       "      <td>The Martian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>142087</td>\n",
       "      <td>B00QXW5GYY</td>\n",
       "      <td>Paula Hawkins</td>\n",
       "      <td>Mysteries &amp; Thrillers</td>\n",
       "      <td>10 hrs and 58 mins</td>\n",
       "      <td>https://www.audible.com/pd/The-Girl-on-the-Tra...</td>\n",
       "      <td>Clare Corbett, Louise Brealey, India Fisher</td>\n",
       "      <td>28.00</td>\n",
       "      <td>4.5</td>\n",
       "      <td>133818.0</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>The Girl on the Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4895</td>\n",
       "      <td>B01IW9TQPK</td>\n",
       "      <td>Trevor Noah</td>\n",
       "      <td>Bios &amp; Memoirs</td>\n",
       "      <td>8 hrs and 44 mins</td>\n",
       "      <td>https://www.audible.com/pd/Born-a-Crime-Audiob...</td>\n",
       "      <td>Trevor Noah</td>\n",
       "      <td>24.95</td>\n",
       "      <td>5.0</td>\n",
       "      <td>123838.0</td>\n",
       "      <td>2016-11-15</td>\n",
       "      <td>Born a Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>282008</td>\n",
       "      <td>B01I28NFEE</td>\n",
       "      <td>Mark Manson</td>\n",
       "      <td>Self Development</td>\n",
       "      <td>5 hrs and 17 mins</td>\n",
       "      <td>https://www.audible.com/pd/The-Subtle-Art-of-N...</td>\n",
       "      <td>Roger Wayne</td>\n",
       "      <td>23.95</td>\n",
       "      <td>4.5</td>\n",
       "      <td>113261.0</td>\n",
       "      <td>2016-09-13</td>\n",
       "      <td>The Subtle Art of Not Giving a F*ck</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index        asin         author               category  \\\n",
       "0  247928  B005FRGT44   Ernest Cline       Sci-Fi & Fantasy   \n",
       "1  248009  B00B5HZGUG      Andy Weir       Sci-Fi & Fantasy   \n",
       "2  142087  B00QXW5GYY  Paula Hawkins  Mysteries & Thrillers   \n",
       "3    4895  B01IW9TQPK    Trevor Noah         Bios & Memoirs   \n",
       "4  282008  B01I28NFEE    Mark Manson       Self Development   \n",
       "\n",
       "               length                                               link  \\\n",
       "0  15 hrs and 40 mins  https://www.audible.com/pd/Ready-Player-One-Au...   \n",
       "1  10 hrs and 53 mins  https://www.audible.com/pd/The-Martian-Audiobo...   \n",
       "2  10 hrs and 58 mins  https://www.audible.com/pd/The-Girl-on-the-Tra...   \n",
       "3   8 hrs and 44 mins  https://www.audible.com/pd/Born-a-Crime-Audiob...   \n",
       "4   5 hrs and 17 mins  https://www.audible.com/pd/The-Subtle-Art-of-N...   \n",
       "\n",
       "                                      narrator  price  rating  rating_count  \\\n",
       "0                                  Wil Wheaton  31.50     5.0      216094.0   \n",
       "1                                   R. C. Bray  29.99     5.0      164988.0   \n",
       "2  Clare Corbett, Louise Brealey, India Fisher  28.00     4.5      133818.0   \n",
       "3                                  Trevor Noah  24.95     5.0      123838.0   \n",
       "4                                  Roger Wayne  23.95     4.5      113261.0   \n",
       "\n",
       "  release_date                                title  \n",
       "0   2011-08-16                     Ready Player One  \n",
       "1   2013-03-22                          The Martian  \n",
       "2   2015-01-13                The Girl on the Train  \n",
       "3   2016-11-15                         Born a Crime  \n",
       "4   2016-09-13  The Subtle Art of Not Giving a F*ck  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure Index vs Cum Reviews\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "top_n = 50000\n",
    "x = list(range(top_n))\n",
    "cumsum = df.iloc[:top_n]['rating_count'].cumsum()/total_ratings\n",
    "plt.plot(x, cumsum);\n",
    "plt.title('Review Index vs. Cumulative Percent of Total Ratings')\n",
    "save_fig('Index vs Cum Reviews')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "436796"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26101"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoff_ind = cumsum[cumsum>0.8].index[0]\n",
    "df = df.iloc[7978:cutoff_ind]\n",
    "cutoff_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_items = df[['asin', 'title', 'author']]\n",
    "program_list = [tuple(x) for x in df_items.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(s):\n",
    "    s = s.replace('.', ' stop ')\n",
    "    s = s.replace('\\n', ' ')\n",
    "    for p in string.punctuation:\n",
    "        s = s.replace(p, '')\n",
    "    return s.lower().strip()\n",
    "\n",
    "\n",
    "def get_overall_rating(asin, rating_dict):\n",
    "    sess = HTMLSession()\n",
    "    r = sess.get(f\"https://www.audible.com/pd/{asin}\")\n",
    "    rating_dict[asin] = np.float(r.html.find('div.bc-row.bc-spacing-small', \n",
    "                                             first=False)[1].text.split(' stars ')[1][:3])\n",
    "    sess.close()\n",
    "    return rating_dict\n",
    "\n",
    "\n",
    "def get_reviews(asin, title, author):\n",
    "    baseurl = f'https://www.audible.com/pd/reviews?country=US&asin={asin}&page='\n",
    "    page_num = 0\n",
    "    ratings_reviews = []\n",
    "    sess = HTMLSession()\n",
    "    title = normalize(title)\n",
    "    try:\n",
    "        author = normalize(author)\n",
    "    except:\n",
    "        author = '<auth>'\n",
    "    \n",
    "    while True:\n",
    "        try:\n",
    "            url = baseurl + str(page_num)\n",
    "            r = sess.get(url).html\n",
    "            page_elements = r.find('div.bc-row-responsive.bc-spacing-top-medium', first=False)\n",
    "            for elem in page_elements:\n",
    "                try:\n",
    "                    review = elem.find(f'div.bc-col-responsive.USreviews{page_num}.bc-col-9', first=True).text\n",
    "                    review = normalize(review)\n",
    "                    review = review.replace(title, '<unk>').replace(author, '<unk>')\n",
    "                    ratings = [item.text[0] for item in elem.find('span.bc-text')]\n",
    "                    ratings_reviews.append((review, *ratings))\n",
    "                except:\n",
    "                    break\n",
    "            page_num += 1\n",
    "            \n",
    "        except:\n",
    "            break\n",
    "    \n",
    "    sess.close()\n",
    "    return ratings_reviews\n",
    "\n",
    "\n",
    "def get_ratings_and_reviews(program_group, rating_dict, reviews):\n",
    "    for asin, title, author in program_group:\n",
    "        try:\n",
    "            rating_dict = (get_overall_rating(asin, rating_dict))\n",
    "        except:\n",
    "            pass\n",
    "        reviews.extend(get_reviews(asin, title, author))\n",
    "#         print(reviews)\n",
    "    return rating_dict, reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def threaded_ratings(nthreads, program_list, rating_dict=None, reviews=None):\n",
    "    if rating_dict == None:\n",
    "        rating_dict = {}\n",
    "        \n",
    "    if reviews == None:\n",
    "        reviews = []\n",
    "        \n",
    "    threads = []\n",
    "    for i in range(nthreads):\n",
    "        program_group = program_list[i::nthreads]\n",
    "        t = Thread(target=get_ratings_and_reviews, args=(program_group, rating_dict, reviews))\n",
    "        threads.append(t)\n",
    "        \n",
    "    [t.start() for t in threads]\n",
    "    [t.join() for t in threads]\n",
    "    \n",
    "    return rating_dict, reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7308.237472772598\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "rating_dict = {}\n",
    "rating_dict, reviews = threaded_ratings(16, program_list[::-1], rating_dict)\n",
    "end = time.time()\n",
    "print(end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "822922"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reviews = pd.DataFrame(data=reviews, columns=['text', 'overall', 'performance', 'story'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reviews.to_csv('reviews2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings = pd.DataFrame.from_dict(data=rating_dict, orient='index', columns=['rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings['asin'] = df_ratings.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('rating', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged = pd.merge(df, df_ratings, on='asin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged.to_csv('8k_top_granular_audible2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}