73 Strings Case Study Amazon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scraping the Data from Amazon.in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bs4\n",
      "  Using cached https://files.pythonhosted.org/packages/10/ed/7e8b97591f6f456174139ec089c769f89a94a1a4025fe967691de971f314/bs4-0.0.1.tar.gz\n",
      "Requirement already satisfied: beautifulsoup4 in c:\\users\\tulas\\anaconda3\\lib\\site-packages (from bs4) (4.8.0)\n",
      "Requirement already satisfied: soupsieve>=1.2 in c:\\users\\tulas\\anaconda3\\lib\\site-packages (from beautifulsoup4->bs4) (1.9.3)\n",
      "Building wheels for collected packages: bs4\n",
      "  Building wheel for bs4 (setup.py): started\n",
      "  Building wheel for bs4 (setup.py): finished with status 'done'\n",
      "  Created wheel for bs4: filename=bs4-0.0.1-cp37-none-any.whl size=1278 sha256=91cab5e7e6cabbdd93075b19ca51e547388aa3e93e3a7a9334d7a624e3341025\n",
      "  Stored in directory: C:\\Users\\tulas\\AppData\\Local\\pip\\Cache\\wheels\\a0\\b0\\b2\\4f80b9456b87abedbc0bf2d52235414c3467d8889be38dd472\n",
      "Successfully built bs4\n",
      "Installing collected packages: bs4\n",
      "Successfully installed bs4-0.0.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install bs4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "header={'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "cookie={} # insert request cookies within{}\n",
    "def getAmazonSearch(search_query,page_number):\n",
    "    url=\"https://www.amazon.in/s?k={search_query}&page={page_number}\".format(search_query = search_query, page_number=page_number)\n",
    "    print(url)\n",
    "    page=requests.get(url,cookies=cookie,headers=header)\n",
    "    if page.status_code==200:\n",
    "        return page\n",
    "    else:\n",
    "        return \"Error\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Searchasin(asin):\n",
    "    url=\"https://www.amazon.in/dp/\"+asin\n",
    "    print(url)\n",
    "    page=requests.get(url,cookies=cookie,headers=header)\n",
    "    if page.status_code==200:\n",
    "        return page\n",
    "    else:\n",
    "        return \"Error\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Searchreviews(review_link):\n",
    "    url=\"https://www.amazon.in\"+review_link\n",
    "    print(url)\n",
    "    page=requests.get(url,cookies=cookie,headers=header)\n",
    "    if page.status_code==200:\n",
    "        return page\n",
    "    else:\n",
    "        return \"Error\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.amazon.in/s?k=smart+phones&page=1\n"
     ]
    }
   ],
   "source": [
    "number_of_pages = 0\n",
    "response=getAmazonSearch('smart+phones',1)\n",
    "soup=BeautifulSoup(response.content)\n",
    "pages = soup.findAll('li',{'class':'a-disabled'})\n",
    "for i in pages[1:]:\n",
    "    number_of_pages=int(i.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.amazon.in/s?k=smart+phones&page=1\n",
      "https://www.amazon.in/s?k=smart+phones&page=2\n",
      "https://www.amazon.in/s?k=smart+phones&page=3\n",
      "https://www.amazon.in/s?k=smart+phones&page=4\n",
      "https://www.amazon.in/s?k=smart+phones&page=5\n",
      "https://www.amazon.in/s?k=smart+phones&page=6\n",
      "https://www.amazon.in/s?k=smart+phones&page=7\n",
      "https://www.amazon.in/s?k=smart+phones&page=8\n",
      "https://www.amazon.in/s?k=smart+phones&page=9\n",
      "https://www.amazon.in/s?k=smart+phones&page=10\n",
      "https://www.amazon.in/s?k=smart+phones&page=11\n",
      "https://www.amazon.in/s?k=smart+phones&page=12\n",
      "https://www.amazon.in/s?k=smart+phones&page=13\n",
      "https://www.amazon.in/s?k=smart+phones&page=14\n",
      "https://www.amazon.in/s?k=smart+phones&page=15\n",
      "https://www.amazon.in/s?k=smart+phones&page=16\n",
      "https://www.amazon.in/s?k=smart+phones&page=17\n",
      "https://www.amazon.in/s?k=smart+phones&page=18\n",
      "https://www.amazon.in/s?k=smart+phones&page=19\n",
      "https://www.amazon.in/s?k=smart+phones&page=20\n"
     ]
    }
   ],
   "source": [
    "data_asin=[]\n",
    "for i in range(1,number_of_pages+1):\n",
    "    response = getAmazonSearch('smart+phones',i)\n",
    "    soup = BeautifulSoup(response.content)\n",
    "    for i in soup.findAll(\"div\",{'class':\"sg-col-20-of-24 s-result-item s-asin sg-col-0-of-12 sg-col-28-of-32 sg-col-16-of-20 sg-col sg-col-32-of-36 sg-col-12-of-16 sg-col-24-of-28\"}):\n",
    "        data_asin.append(i['data-asin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_asin = set(data_asin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_asin = list(data_asin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.amazon.in/dp/B07HGLM366\n",
      "https://www.amazon.in/dp/B086984LJ4\n",
      "https://www.amazon.in/dp/B082FP7T27\n",
      "https://www.amazon.in/dp/B08444Z7R9\n",
      "https://www.amazon.in/dp/B07HGJJ559\n",
      "https://www.amazon.in/dp/B078BNQ318\n",
      "https://www.amazon.in/dp/B0859RGNB4\n",
      "https://www.amazon.in/dp/B086982ZKF\n",
      "https://www.amazon.in/dp/B08CGHK8QM\n",
      "https://www.amazon.in/dp/B077PW9ZQL\n",
      "https://www.amazon.in/dp/B086KCCSDH\n",
      "https://www.amazon.in/dp/B089N3877P\n",
      "https://www.amazon.in/dp/B07X4PXKZ7\n",
      "https://www.amazon.in/dp/B086KCC67C\n",
      "https://www.amazon.in/dp/B07X4PW616\n",
      "https://www.amazon.in/dp/B07DJCJBB3\n",
      "https://www.amazon.in/dp/B07N8SDZ4M\n",
      "https://www.amazon.in/dp/B07XVM1KVY\n",
      "https://www.amazon.in/dp/B07HGLC2CG\n",
      "https://www.amazon.in/dp/B07DJCYBVK\n",
      "https://www.amazon.in/dp/B08185WJL7\n",
      "https://www.amazon.in/dp/B08334RTFV\n",
      "https://www.amazon.in/dp/B07X1KSLCB\n",
      "https://www.amazon.in/dp/B07X8QTSL4\n",
      "https://www.amazon.in/dp/B08FG7WMV6\n",
      "https://www.amazon.in/dp/B07HGGYWL6\n",
      "https://www.amazon.in/dp/B089MTL51J\n",
      "https://www.amazon.in/dp/B07XD2HY9L\n",
      "https://www.amazon.in/dp/B089MSCW8P\n",
      "https://www.amazon.in/dp/B07SCP71LH\n",
      "https://www.amazon.in/dp/B086KCC68C\n",
      "https://www.amazon.in/dp/B07Z6GKRS7\n",
      "https://www.amazon.in/dp/B086K9CW1W\n",
      "https://www.amazon.in/dp/B08699CZBC\n",
      "https://www.amazon.in/dp/B0784D7NFQ\n",
      "https://www.amazon.in/dp/B07KXC7WTR\n",
      "https://www.amazon.in/dp/B08BG24YF1\n",
      "https://www.amazon.in/dp/B07X4PXKZQ\n",
      "https://www.amazon.in/dp/B08695ZSP6\n",
      "https://www.amazon.in/dp/B07YMZJ733\n",
      "https://www.amazon.in/dp/B08DLWS6CN\n",
      "https://www.amazon.in/dp/B07BNHHKV8\n",
      "https://www.amazon.in/dp/B08CZPWWCN\n",
      "https://www.amazon.in/dp/B07HGN617M\n",
      "https://www.amazon.in/dp/B086KCC68B\n",
      "https://www.amazon.in/dp/B086977TR6\n",
      "https://www.amazon.in/dp/B08444XXKV\n",
      "https://www.amazon.in/dp/B07ZWYZ949\n",
      "https://www.amazon.in/dp/B07HGJK56Y\n",
      "https://www.amazon.in/dp/B08CSL4MHT\n",
      "https://www.amazon.in/dp/B082633VDJ\n",
      "https://www.amazon.in/dp/B077Q44DKY\n",
      "https://www.amazon.in/dp/B08B3WKTWR\n",
      "https://www.amazon.in/dp/B07PQ7CXKG\n",
      "https://www.amazon.in/dp/B0856C1WZ1\n",
      "https://www.amazon.in/dp/B087K78FD5\n",
      "https://www.amazon.in/dp/B087K7ZFB6\n",
      "https://www.amazon.in/dp/B07XVMDRZY\n",
      "https://www.amazon.in/dp/B07XVLMXYH\n",
      "https://www.amazon.in/dp/B07YFVDPWM\n",
      "https://www.amazon.in/dp/B07SDPJ4XJ\n",
      "https://www.amazon.in/dp/B01LZWIOS4\n",
      "https://www.amazon.in/dp/B07GZ5BX6P\n",
      "https://www.amazon.in/dp/B07ZS1XL59\n",
      "https://www.amazon.in/dp/B08DK9QSXX\n",
      "https://www.amazon.in/dp/B08F1ZHHMS\n",
      "https://www.amazon.in/dp/B07XVMJF2D\n",
      "https://www.amazon.in/dp/B08DG2MN26\n",
      "https://www.amazon.in/dp/B08FCBMX26\n",
      "https://www.amazon.in/dp/B08444WMGT\n",
      "https://www.amazon.in/dp/B07X4R63DF\n",
      "https://www.amazon.in/dp/B08D41TXGF\n",
      "https://www.amazon.in/dp/B086977J3K\n",
      "https://www.amazon.in/dp/B08696XM8J\n",
      "https://www.amazon.in/dp/B08DNNQ235\n",
      "https://www.amazon.in/dp/B08CKV2KJJ\n",
      "https://www.amazon.in/dp/B07S6BGL1K\n",
      "https://www.amazon.in/dp/B087K6SWBK\n",
      "https://www.amazon.in/dp/B07H6NJX5F\n",
      "https://www.amazon.in/dp/B071HWTHBK\n",
      "https://www.amazon.in/dp/B084R5FJDV\n",
      "https://www.amazon.in/dp/B08CNCB15D\n",
      "https://www.amazon.in/dp/B08697WT79\n",
      "https://www.amazon.in/dp/B07QVCMRK6\n",
      "https://www.amazon.in/dp/B08CVNP4YP\n",
      "https://www.amazon.in/dp/B08CDY93KR\n",
      "https://www.amazon.in/dp/B07HK4JNV1\n",
      "https://www.amazon.in/dp/B07ZFSLBZW\n",
      "https://www.amazon.in/dp/B08CY6NGD7\n",
      "https://www.amazon.in/dp/B07ZWZ5K4P\n",
      "https://www.amazon.in/dp/B086KDZGTZ\n",
      "https://www.amazon.in/dp/B07X1KT6LF\n",
      "https://www.amazon.in/dp/B07HGJKDQL\n",
      "https://www.amazon.in/dp/B07PSHDTHB\n",
      "https://www.amazon.in/dp/B07X9YNV63\n",
      "https://www.amazon.in/dp/B0849NJJ43\n",
      "https://www.amazon.in/dp/B07X1KT6LD\n",
      "https://www.amazon.in/dp/B07XBYMDTH\n",
      "https://www.amazon.in/dp/B07DJLVJ5M\n",
      "https://www.amazon.in/dp/B07HGH8D2R\n",
      "https://www.amazon.in/dp/B07HGH88GL\n",
      "https://www.amazon.in/dp/B08CZMZGTW\n",
      "https://www.amazon.in/dp/B0824BR684\n",
      "https://www.amazon.in/dp/B085W2P4WV\n",
      "https://www.amazon.in/dp/B07ST1SG9F\n",
      "https://www.amazon.in/dp/B085W2YQ1F\n",
      "https://www.amazon.in/dp/B08DV3LWY9\n",
      "https://www.amazon.in/dp/B07MBP65HK\n",
      "https://www.amazon.in/dp/B0817P7J74\n",
      "https://www.amazon.in/dp/B089MQ765M\n",
      "https://www.amazon.in/dp/B07TWXNL3F\n",
      "https://www.amazon.in/dp/B083WLGD3N\n",
      "https://www.amazon.in/dp/B086K9XK6F\n",
      "https://www.amazon.in/dp/B08BV2T63X\n",
      "https://www.amazon.in/dp/B071Z97T2C\n",
      "https://www.amazon.in/dp/B07RY2NR8F\n",
      "https://www.amazon.in/dp/B08DG4XPBC\n",
      "https://www.amazon.in/dp/B086KDZGVK\n",
      "https://www.amazon.in/dp/B08CVNQ8B7\n",
      "https://www.amazon.in/dp/B089MQ765W\n"
     ]
    }
   ],
   "source": [
    "link=[]\n",
    "for i in data_asin[:120]:\n",
    "    response=Searchasin(i)\n",
    "    soup=BeautifulSoup(response.content)\n",
    "    for i in soup.findAll(\"a\",{'data-hook':\"see-all-reviews-link-foot\"}):\n",
    "        link.append(i['href'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=1\n",
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=2\n",
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=3\n",
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=4\n",
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=5\n",
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=6\n",
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=7\n",
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=8\n",
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=9\n",
      "https://www.amazon.in/Samsung-Galaxy-Space-Black-Storage/product-reviews/B07HGLM366/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=10\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=1\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=2\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=3\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=4\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=5\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=6\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=7\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=8\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=9\n",
      "https://www.amazon.in/Redmi-K20-Pro-Carbon-Storage/product-reviews/B082FP7T27/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=10\n",
      "https://www.amazon.in/OPPO-Storage-Additional-Exchange-Offers/product-reviews/B08444Z7R9/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=1\n",
      "https://www.amazon.in/OPPO-Storage-Additional-Exchange-Offers/product-reviews/B08444Z7R9/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=2\n",
      "https://www.amazon.in/OPPO-Storage-Additional-Exchange-Offers/product-reviews/B08444Z7R9/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=3\n",
      "https://www.amazon.in/OPPO-Storage-Additional-Exchange-Offers/product-reviews/B08444Z7R9/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=4\n",
      "https://www.amazon.in/OPPO-Storage-Additional-Exchange-Offers/product-reviews/B08444Z7R9/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=5\n",
      "https://www.amazon.in/OPPO-Storage-Additional-Exchange-Offers/product-reviews/B08444Z7R9/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=6\n",
      "https://www.amazon.in/OPPO-Storage-Additional-Exchange-Offers/product-reviews/B08444Z7R9/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=7\n",
      "https://www.amazon.in/OPPO-Storage-Additional-Exchange-Offers/product-reviews/B08444Z7R9/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=8\n",
      "https://www.amazon.in/OPPO-Storage-Additional-Exchange-Offers/product-reviews/B08444Z7R9/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=9\n",
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      "https://www.amazon.in/Nokia-4-2-Black-32GB-Storage/product-reviews/B07RY2NR8F/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=2\n",
      "https://www.amazon.in/Nokia-4-2-Black-32GB-Storage/product-reviews/B07RY2NR8F/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=3\n",
      "https://www.amazon.in/Nokia-4-2-Black-32GB-Storage/product-reviews/B07RY2NR8F/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=4\n",
      "https://www.amazon.in/Nokia-4-2-Black-32GB-Storage/product-reviews/B07RY2NR8F/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=5\n",
      "https://www.amazon.in/Nokia-4-2-Black-32GB-Storage/product-reviews/B07RY2NR8F/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=6\n",
      "https://www.amazon.in/Nokia-4-2-Black-32GB-Storage/product-reviews/B07RY2NR8F/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=7\n",
      "https://www.amazon.in/Nokia-4-2-Black-32GB-Storage/product-reviews/B07RY2NR8F/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=8\n",
      "https://www.amazon.in/Nokia-4-2-Black-32GB-Storage/product-reviews/B07RY2NR8F/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=9\n",
      "https://www.amazon.in/Nokia-4-2-Black-32GB-Storage/product-reviews/B07RY2NR8F/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=10\n",
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=1\n",
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=3\n",
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=4\n",
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=5\n",
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=6\n",
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=7\n",
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=8\n",
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=9\n",
      "https://www.amazon.in/Realme-C11-Rich-Green-RAM/product-reviews/B08DG4XPBC/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=10\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=1\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=2\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=3\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=4\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=5\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=6\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=7\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=8\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=9\n",
      "https://www.amazon.in/Samsung-Galaxy-Storage-Additional-Exchange/product-reviews/B086KDZGVK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=10\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=1\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=2\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=3\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=4\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=5\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=6\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=7\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=8\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=9\n",
      "https://www.amazon.in/Green-Storage-Additional-Exchange-Offers/product-reviews/B089MQ765W/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews&pageNumber=10\n"
     ]
    }
   ],
   "source": [
    "reviews=[]\n",
    "product_name = []\n",
    "for j in range(len(link)):\n",
    "    for k in range(1,11):\n",
    "        response=Searchreviews(link[j]+'&pageNumber='+str(k))\n",
    "        soup = BeautifulSoup(response.content)\n",
    "        for i in soup.findAll('a',{'data-hook':'review-title'}):\n",
    "            reviews.append(i.text)\n",
    "            for i in soup.findAll('a',{'data-hook':'product-link'}):\n",
    "                product_name.append(i.text)\n",
    "                \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7911"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(product_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "rev_dic = {'product_name':product_name, 'reviews':reviews}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_data=pd.DataFrame.from_dict(rev_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_data.to_csv(r'E:\\73 strings\\custom\\Scraped_reviews.csv',index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_data = pd.read_csv(r'E:\\73 strings\\custom\\Scraped_reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>product_name</th>\n",
       "      <th>reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>\\nSamsung m31 all rounder  best mobile.\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>\\nBest Value for Money in this Range. Go for i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>\\nPoor camera\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>\\nFaulty product\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>\\nFalse ads, poor camera quality, inefficient ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7906</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>\\nNice product\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7907</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>\\nMind blowing\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7908</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>\\nOppo A31\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7909</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>\\nWaste\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7910</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>\\ngood product\\n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7911 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           product_name  \\\n",
       "0     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "1     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "2     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "3     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "4     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "...                                                 ...   \n",
       "7906  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7907  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7908  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7909  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7910  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "\n",
       "                                                reviews  \n",
       "0             \\nSamsung m31 all rounder  best mobile.\\n  \n",
       "1     \\nBest Value for Money in this Range. Go for i...  \n",
       "2                                       \\nPoor camera\\n  \n",
       "3                                    \\nFaulty product\\n  \n",
       "4     \\nFalse ads, poor camera quality, inefficient ...  \n",
       "...                                                 ...  \n",
       "7906                                   \\nNice product\\n  \n",
       "7907                                   \\nMind blowing\\n  \n",
       "7908                                       \\nOppo A31\\n  \n",
       "7909                                          \\nWaste\\n  \n",
       "7910                                   \\ngood product\\n  \n",
       "\n",
       "[7911 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REVIEWS PREPROCESSING PART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "df=review_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['reviews']=df['reviews'].apply(lambda x:x.strip('\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>product_name</th>\n",
       "      <th>reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Samsung m31 all rounder  best mobile.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Best Value for Money in this Range. Go for it ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Poor camera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Faulty product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>False ads, poor camera quality, inefficient ba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7906</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>Nice product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7907</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>Mind blowing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7908</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>Oppo A31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7909</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>Waste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7910</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>good product</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7911 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           product_name  \\\n",
       "0     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "1     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "2     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "3     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "4     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "...                                                 ...   \n",
       "7906  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7907  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7908  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7909  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7910  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "\n",
       "                                                reviews  \n",
       "0                 Samsung m31 all rounder  best mobile.  \n",
       "1     Best Value for Money in this Range. Go for it ...  \n",
       "2                                           Poor camera  \n",
       "3                                        Faulty product  \n",
       "4     False ads, poor camera quality, inefficient ba...  \n",
       "...                                                 ...  \n",
       "7906                                       Nice product  \n",
       "7907                                       Mind blowing  \n",
       "7908                                           Oppo A31  \n",
       "7909                                              Waste  \n",
       "7910                                       good product  \n",
       "\n",
       "[7911 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import PorterStemmer\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "STOPWORDS=stopwords.words(\"english\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deEmojify(inputString):\n",
    "    return inputString.encode('ascii', 'ignore').decode('ascii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string   \n",
    "import re\n",
    "\n",
    "def clean_text(text):\n",
    "    ps=PorterStemmer()\n",
    "    \n",
    "    text=deEmojify(text) # remove emojis\n",
    "    text_cleaned=\"\".join([x for x in text if x not in string.punctuation]) # remove punctuation\n",
    "    \n",
    "    text_cleaned=re.sub(' +', ' ', text_cleaned) # remove extra white spaces\n",
    "    text_cleaned=text_cleaned.lower() # converting to lowercase\n",
    "    tokens=text_cleaned.split(\" \")\n",
    "    tokens=[token for token in tokens if token not in STOPWORDS] # Taking only those words which are not stopwords\n",
    "    text_cleaned=\" \".join([ps.stem(token) for token in tokens])\n",
    "    \n",
    "    \n",
    "    return text_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cleaned_reviews']=df['reviews'].apply(lambda x:clean_text(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>product_name</th>\n",
       "      <th>reviews</th>\n",
       "      <th>cleaned_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Samsung m31 all rounder  best mobile.</td>\n",
       "      <td>samsung m31 rounder best mobil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Best Value for Money in this Range. Go for it ...</td>\n",
       "      <td>best valu money rang go without doubt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Poor camera</td>\n",
       "      <td>poor camera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Faulty product</td>\n",
       "      <td>faulti product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>False ads, poor camera quality, inefficient ba...</td>\n",
       "      <td>fals ad poor camera qualiti ineffici batteri m...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        product_name  \\\n",
       "0  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "1  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "2  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "3  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "4  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "\n",
       "                                             reviews  \\\n",
       "0              Samsung m31 all rounder  best mobile.   \n",
       "1  Best Value for Money in this Range. Go for it ...   \n",
       "2                                        Poor camera   \n",
       "3                                     Faulty product   \n",
       "4  False ads, poor camera quality, inefficient ba...   \n",
       "\n",
       "                                     cleaned_reviews  \n",
       "0                     samsung m31 rounder best mobil  \n",
       "1              best valu money rang go without doubt  \n",
       "2                                        poor camera  \n",
       "3                                     faulti product  \n",
       "4  fals ad poor camera qualiti ineffici batteri m...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = df['cleaned_reviews']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tulas\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_1.to_csv('E:/73 strings/custom/test_reviews.txt', header=None, index=None, mode='a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAINING THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(\n",
    "            [\n",
    "                ('vect', CountVectorizer()),\n",
    "                ('tfidf', TfidfTransformer()),\n",
    "                ('clf', SGDClassifier(\n",
    "                    loss='hinge',\n",
    "                    penalty='l2',\n",
    "                    alpha=1e-3,\n",
    "                    random_state=42,\n",
    "                    max_iter=100,\n",
    "                    learning_rate='optimal',\n",
    "                    tol=None,\n",
    "                )),\n",
    "            ]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install pytreebank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "import pytreebank\n",
    "import sys\n",
    "import os\n",
    "\n",
    "dataset = pytreebank.load_sst()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_path = (\"E:/73 strings/custom/sst_{}.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for category in ['train', 'test', 'dev']:\n",
    "    with open(out_path.format(category), 'w') as outfile:\n",
    "        for item in dataset[category]:\n",
    "            outfile.write(\"__label__{}\\t{}\\n\".format(\n",
    "                item.to_labeled_lines()[0][0] + 1,\n",
    "                item.to_labeled_lines()[0][1]\n",
    "            ))"
   ]
  },
  {
   "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>truth</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>The Rock is destined to be the 21st Century 's...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>The gorgeously elaborate continuation of `` Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Singer/composer Bryan Adams contributes a slew...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>You 'd think by now America would have had eno...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Yet the act is still charming here .</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  truth                                               text\n",
       "0     4  The Rock is destined to be the 21st Century 's...\n",
       "1     5  The gorgeously elaborate continuation of `` Th...\n",
       "2     4  Singer/composer Bryan Adams contributes a slew...\n",
       "3     3  You 'd think by now America would have had eno...\n",
       "4     4               Yet the act is still charming here ."
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd# Read train data\n",
    "train_df = pd.read_table('E:/73 strings/custom/sst_train.txt', sep='\\t',encoding='ISO-8859-1', header=None, names=['truth', 'text'])\n",
    "train_df['truth'] = train_df['truth'].str.replace('__label__', '')\n",
    "train_df['truth'] = train_df['truth'].astype(int).astype('category')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = pipeline.fit(train_df['text'], train_df['truth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = learner.predict(df['cleaned_reviews'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pred']= list(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "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>product_name</th>\n",
       "      <th>reviews</th>\n",
       "      <th>cleaned_reviews</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Samsung m31 all rounder  best mobile.</td>\n",
       "      <td>samsung m31 rounder best mobil</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Best Value for Money in this Range. Go for it ...</td>\n",
       "      <td>best valu money rang go without doubt</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Poor camera</td>\n",
       "      <td>poor camera</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Faulty product</td>\n",
       "      <td>faulti product</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>False ads, poor camera quality, inefficient ba...</td>\n",
       "      <td>fals ad poor camera qualiti ineffici batteri m...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7906</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>Nice product</td>\n",
       "      <td>nice product</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7907</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>Mind blowing</td>\n",
       "      <td>mind blow</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7908</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>Oppo A31</td>\n",
       "      <td>oppo a31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7909</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>Waste</td>\n",
       "      <td>wast</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7910</td>\n",
       "      <td>OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...</td>\n",
       "      <td>good product</td>\n",
       "      <td>good product</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7911 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           product_name  \\\n",
       "0     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "1     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "2     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "3     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "4     Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "...                                                 ...   \n",
       "7906  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7907  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7908  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7909  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "7910  OPPO A31 (Lake Green, 6GB RAM, 128GB Storage) ...   \n",
       "\n",
       "                                                reviews  \\\n",
       "0                 Samsung m31 all rounder  best mobile.   \n",
       "1     Best Value for Money in this Range. Go for it ...   \n",
       "2                                           Poor camera   \n",
       "3                                        Faulty product   \n",
       "4     False ads, poor camera quality, inefficient ba...   \n",
       "...                                                 ...   \n",
       "7906                                       Nice product   \n",
       "7907                                       Mind blowing   \n",
       "7908                                           Oppo A31   \n",
       "7909                                              Waste   \n",
       "7910                                       good product   \n",
       "\n",
       "                                        cleaned_reviews  pred  \n",
       "0                        samsung m31 rounder best mobil     5  \n",
       "1                 best valu money rang go without doubt     5  \n",
       "2                                           poor camera     1  \n",
       "3                                        faulti product     2  \n",
       "4     fals ad poor camera qualiti ineffici batteri m...     3  \n",
       "...                                                 ...   ...  \n",
       "7906                                       nice product     2  \n",
       "7907                                          mind blow     2  \n",
       "7908                                           oppo a31     2  \n",
       "7909                                               wast     2  \n",
       "7910                                       good product     2  \n",
       "\n",
       "[7911 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_category(score):\n",
    "    if score == 1:\n",
    "        return \"strongly negative\"\n",
    "    elif score == 2:\n",
    "        return \"weakly negative\"\n",
    "    elif score == 3:\n",
    "        return \"neutral\"\n",
    "    elif score == 4:\n",
    "        return \"weakly positive\"\n",
    "    elif score == 5:\n",
    "        return \"strongly positive\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review_category']=df['pred'].apply(lambda x:sentiment_category(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_name</th>\n",
       "      <th>reviews</th>\n",
       "      <th>cleaned_reviews</th>\n",
       "      <th>pred</th>\n",
       "      <th>review_category</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Samsung m31 all rounder  best mobile.</td>\n",
       "      <td>samsung m31 rounder best mobil</td>\n",
       "      <td>5</td>\n",
       "      <td>strongly positive</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Best Value for Money in this Range. Go for it ...</td>\n",
       "      <td>best valu money rang go without doubt</td>\n",
       "      <td>5</td>\n",
       "      <td>strongly positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Poor camera</td>\n",
       "      <td>poor camera</td>\n",
       "      <td>1</td>\n",
       "      <td>strongly negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>Faulty product</td>\n",
       "      <td>faulti product</td>\n",
       "      <td>2</td>\n",
       "      <td>weakly negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...</td>\n",
       "      <td>False ads, poor camera quality, inefficient ba...</td>\n",
       "      <td>fals ad poor camera qualiti ineffici batteri m...</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        product_name  \\\n",
       "0  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "1  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "2  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "3  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "4  Samsung Galaxy M31 (Space Black, 8GB RAM, 128G...   \n",
       "\n",
       "                                             reviews  \\\n",
       "0              Samsung m31 all rounder  best mobile.   \n",
       "1  Best Value for Money in this Range. Go for it ...   \n",
       "2                                        Poor camera   \n",
       "3                                     Faulty product   \n",
       "4  False ads, poor camera quality, inefficient ba...   \n",
       "\n",
       "                                     cleaned_reviews  pred    review_category  \n",
       "0                     samsung m31 rounder best mobil     5  strongly positive  \n",
       "1              best valu money rang go without doubt     5  strongly positive  \n",
       "2                                        poor camera     1  strongly negative  \n",
       "3                                     faulti product     2    weakly negative  \n",
       "4  fals ad poor camera qualiti ineffici batteri m...     3            neutral  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df.head(5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Reviews Category')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(df['review_category']).set_title(\"Distribution of Reviews Category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['samsung m31 rounder best mobil',\n",
       " 'best valu money rang go without doubt',\n",
       " 'best mobil valu money',\n",
       " 'heat charg camera issu m31',\n",
       " 'good perfect']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "strongly_positive_reviews=df.loc[df['review_category']=='strongly positive','cleaned_reviews'].tolist() # extracting all positive reviews and converting to a list\n",
    "strongly_positive_reviews[0:5]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "wordcloud = WordCloud(height=2000, width=2000, background_color='black')\n",
    "wordcloud = wordcloud.generate(' '.join(df.loc[df['review_category']=='strongly positive','cleaned_reviews'].tolist()))\n",
    "plt.imshow(wordcloud)\n",
    "plt.title(\"Most common words in strongly positive customer comments\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "wordcloud = WordCloud(height=2000, width=2000, background_color='black')\n",
    "wordcloud = wordcloud.generate(' '.join(df.loc[df['review_category']=='strongly negative','cleaned_reviews'].tolist()))\n",
    "plt.imshow(wordcloud)\n",
    "plt.title(\"Most common words in strongly negative customer comments\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_strongly_positive = df.loc[df['review_category']=='strongly positive','product_name'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_list_positive = products_strongly_positive[0:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb77f6e508>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(plot_list_positive)\n",
    "\n",
    "#these are the mobiles with strongly positive count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_strongly_negative = df.loc[df['review_category']=='strongly negative','product_name'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_list_negative = products_strongly_negative[300:325]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb78594088>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(plot_list_negative)\n",
    "\n",
    "#mobiles with strongly negative reviews"
   ]
  },
  {
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}