Cleaning Reviews.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3a6f9f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9fa95d57",
   "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>Hotel_Address</th>\n",
       "      <th>Additional_Number_of_Scoring</th>\n",
       "      <th>Review_Date</th>\n",
       "      <th>Average_Score</th>\n",
       "      <th>Hotel_Name</th>\n",
       "      <th>Reviewer_Nationality</th>\n",
       "      <th>Negative_Review</th>\n",
       "      <th>Review_Total_Negative_Word_Counts</th>\n",
       "      <th>Total_Number_of_Reviews</th>\n",
       "      <th>Positive_Review</th>\n",
       "      <th>Review_Total_Positive_Word_Counts</th>\n",
       "      <th>Total_Number_of_Reviews_Reviewer_Has_Given</th>\n",
       "      <th>Reviewer_Score</th>\n",
       "      <th>Tags</th>\n",
       "      <th>days_since_review</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>8/3/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Russia</td>\n",
       "      <td>I am so angry that i made this post available...</td>\n",
       "      <td>397</td>\n",
       "      <td>1403</td>\n",
       "      <td>Only the park outside of the hotel was beauti...</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Duplex Double...</td>\n",
       "      <td>0 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>8/3/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>No Negative</td>\n",
       "      <td>0</td>\n",
       "      <td>1403</td>\n",
       "      <td>No real complaints the hotel was great great ...</td>\n",
       "      <td>105</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Duplex Double...</td>\n",
       "      <td>0 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/31/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Rooms are nice but for elderly a bit difficul...</td>\n",
       "      <td>42</td>\n",
       "      <td>1403</td>\n",
       "      <td>Location was good and staff were ok It is cut...</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>[' Leisure trip ', ' Family with young childre...</td>\n",
       "      <td>3 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Hotel_Address  \\\n",
       "0   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "1   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "2   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "\n",
       "   Additional_Number_of_Scoring Review_Date  Average_Score   Hotel_Name  \\\n",
       "0                           194    8/3/2017            7.7  Hotel Arena   \n",
       "1                           194    8/3/2017            7.7  Hotel Arena   \n",
       "2                           194   7/31/2017            7.7  Hotel Arena   \n",
       "\n",
       "  Reviewer_Nationality                                    Negative_Review  \\\n",
       "0              Russia    I am so angry that i made this post available...   \n",
       "1             Ireland                                         No Negative   \n",
       "2           Australia    Rooms are nice but for elderly a bit difficul...   \n",
       "\n",
       "   Review_Total_Negative_Word_Counts  Total_Number_of_Reviews  \\\n",
       "0                                397                     1403   \n",
       "1                                  0                     1403   \n",
       "2                                 42                     1403   \n",
       "\n",
       "                                     Positive_Review  \\\n",
       "0   Only the park outside of the hotel was beauti...   \n",
       "1   No real complaints the hotel was great great ...   \n",
       "2   Location was good and staff were ok It is cut...   \n",
       "\n",
       "   Review_Total_Positive_Word_Counts  \\\n",
       "0                                 11   \n",
       "1                                105   \n",
       "2                                 21   \n",
       "\n",
       "   Total_Number_of_Reviews_Reviewer_Has_Given  Reviewer_Score  \\\n",
       "0                                           7             2.9   \n",
       "1                                           7             7.5   \n",
       "2                                           9             7.1   \n",
       "\n",
       "                                                Tags days_since_review  \\\n",
       "0  [' Leisure trip ', ' Couple ', ' Duplex Double...            0 days   \n",
       "1  [' Leisure trip ', ' Couple ', ' Duplex Double...            0 days   \n",
       "2  [' Leisure trip ', ' Family with young childre...            3 days   \n",
       "\n",
       "         lat       lng  \n",
       "0  52.360576  4.915968  \n",
       "1  52.360576  4.915968  \n",
       "2  52.360576  4.915968  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Hotel_Reviews.csv\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c5d7974",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9569b5b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 515738 entries, 0 to 515737\n",
      "Data columns (total 17 columns):\n",
      " #   Column                                      Non-Null Count   Dtype  \n",
      "---  ------                                      --------------   -----  \n",
      " 0   Hotel_Address                               515738 non-null  object \n",
      " 1   Additional_Number_of_Scoring                515738 non-null  int64  \n",
      " 2   Review_Date                                 515738 non-null  object \n",
      " 3   Average_Score                               515738 non-null  float64\n",
      " 4   Hotel_Name                                  515738 non-null  object \n",
      " 5   Reviewer_Nationality                        515738 non-null  object \n",
      " 6   Negative_Review                             515738 non-null  object \n",
      " 7   Review_Total_Negative_Word_Counts           515738 non-null  int64  \n",
      " 8   Total_Number_of_Reviews                     515738 non-null  int64  \n",
      " 9   Positive_Review                             515738 non-null  object \n",
      " 10  Review_Total_Positive_Word_Counts           515738 non-null  int64  \n",
      " 11  Total_Number_of_Reviews_Reviewer_Has_Given  515738 non-null  int64  \n",
      " 12  Reviewer_Score                              515738 non-null  float64\n",
      " 13  Tags                                        515738 non-null  object \n",
      " 14  days_since_review                           515738 non-null  object \n",
      " 15  lat                                         512470 non-null  float64\n",
      " 16  lng                                         512470 non-null  float64\n",
      "dtypes: float64(4), int64(5), object(8)\n",
      "memory usage: 66.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a016592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163 Marsh Wall Docklands Tower Hamlets London E14 9SJ United Kingdom              4789\n",
       "372 Strand Westminster Borough London WC2R 0JJ United Kingdom                     4256\n",
       "Westminster Bridge Road Lambeth London SE1 7UT United Kingdom                     4169\n",
       "Scarsdale Place Kensington Kensington and Chelsea London W8 5SY United Kingdom    3578\n",
       "7 Pepys Street City of London London EC3N 4AF United Kingdom                      3212\n",
       "                                                                                  ... \n",
       "21 rue Lavoisier 8th arr 75008 Paris France                                         12\n",
       "2 4 Boundary Street Hackney London E2 7DD United Kingdom                            12\n",
       "Via M Buonarroti 13 Fiera Milano City 20149 Milan Italy                             10\n",
       "13 Rue Fran ois Ory 92120 Paris France                                              10\n",
       "Johann Staud Stra e 32 16 Ottakring 1160 Vienna Austria                              8\n",
       "Name: Hotel_Address, Length: 1493, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Hotel_Address.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "789624ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1493,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Hotel_Address.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2a77cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8/2/2017      2585\n",
       "9/15/2016     2308\n",
       "4/5/2017      2284\n",
       "8/30/2016     1963\n",
       "2/16/2016     1940\n",
       "              ... \n",
       "9/10/2016      199\n",
       "11/27/2015     194\n",
       "4/3/2017       192\n",
       "4/1/2017       173\n",
       "4/2/2017       143\n",
       "Name: Review_Date, Length: 731, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Review_Date.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab13dd46",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = df.Review_Date.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc4561e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['8/2/2017', '9/15/2016', '4/5/2017', '8/30/2016', '2/16/2016',\n",
       "       '7/5/2016', '5/31/2016', '12/5/2016', '7/12/2016', '8/2/2016'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b4ccf1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = dates.index, y = dates)\n",
    "plt.title(\"Most Common Dates\", fontdict = {\"fontsize\" : 14})\n",
    "plt.xlabel(\"Review Date\", fontdict = {\"fontsize\" : 14})\n",
    "plt.ylabel(\"Count\", fontdict = {\"fontsize\" : 14})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93feea2b",
   "metadata": {},
   "source": [
    "## Hotel Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fcb021a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1492,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Hotel_Name.unique().shape#1492 farklı hotel ismi var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92d338a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Britannia International Hotel Canary Wharf           4789\n",
       "Strand Palace Hotel                                  4256\n",
       "Park Plaza Westminster Bridge London                 4169\n",
       "Copthorne Tara Hotel London Kensington               3578\n",
       "DoubleTree by Hilton Hotel London Tower of London    3212\n",
       "                                                     ... \n",
       "Ibis Styles Milano Palmanova                           12\n",
       "Hotel Eitlj rg                                         12\n",
       "Hotel Wagner                                           10\n",
       "Mercure Paris Porte d Orleans                          10\n",
       "Hotel Gallitzinberg                                     8\n",
       "Name: Hotel_Name, Length: 1492, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Hotel_Name.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a67447a",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = df.Hotel_Name.value_counts(dropna = False)[:10]## ençok yorum alan 10 otel ismi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ad5faa0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = names.index, y = names)\n",
    "plt.title(\"Most Reviews Hotel\", fontdict = {\"fontsize\" : 14})\n",
    "plt.xlabel(\"Hotel Name\", fontdict = {\"fontsize\" : 14})\n",
    "plt.ylabel(\"Reviews Count\", fontdict = {\"fontsize\" : 14})\n",
    "plt.xticks(rotation=80);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b2917ca",
   "metadata": {},
   "source": [
    "## Reviewer Nationality                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9de2b530",
   "metadata": {},
   "outputs": [],
   "source": [
    "nationality = df.Reviewer_Nationality.value_counts(dropna = False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ce6e3288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Reviewer_Nationality.unique().shape## 227 farklı milleten yorum var. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ce3f0ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = nationality.index, y = nationality)\n",
    "plt.title(\"Nationality of Reviewer\", fontdict = {\"fontsize\" : 14})\n",
    "plt.xlabel(\"Nationality\", fontdict = {\"fontsize\" : 14})\n",
    "plt.ylabel(\"Reviews Count\", fontdict = {\"fontsize\" : 14})\n",
    "plt.xticks(rotation=80);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48104052",
   "metadata": {},
   "source": [
    "## All Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "06601152",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy[\"Negative_Review\"] = [i.strip() for i in df_copy[\"Negative_Review\"]]# stripingin negative reviews\n",
    "df_copy[\"Positive_Review\"] = [i.strip() for i in df_copy[\"Positive_Review\"]] #stripingin positive reviews\n",
    "df_copy[\"All_Review\"] = df_copy[\"Negative_Review\"] + \" \" + df_copy[\"Positive_Review\"] \n",
    "#We combine 2 reviews and create the all review featuer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "19d47aab",
   "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>Hotel_Address</th>\n",
       "      <th>Additional_Number_of_Scoring</th>\n",
       "      <th>Review_Date</th>\n",
       "      <th>Average_Score</th>\n",
       "      <th>Hotel_Name</th>\n",
       "      <th>Reviewer_Nationality</th>\n",
       "      <th>Negative_Review</th>\n",
       "      <th>Review_Total_Negative_Word_Counts</th>\n",
       "      <th>Total_Number_of_Reviews</th>\n",
       "      <th>Positive_Review</th>\n",
       "      <th>Review_Total_Positive_Word_Counts</th>\n",
       "      <th>Total_Number_of_Reviews_Reviewer_Has_Given</th>\n",
       "      <th>Reviewer_Score</th>\n",
       "      <th>Tags</th>\n",
       "      <th>days_since_review</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>All_Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>8/3/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Russia</td>\n",
       "      <td>I am so angry that i made this post available ...</td>\n",
       "      <td>397</td>\n",
       "      <td>1403</td>\n",
       "      <td>Only the park outside of the hotel was beautiful</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Duplex Double...</td>\n",
       "      <td>0 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>I am so angry that i made this post available ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>8/3/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>No Negative</td>\n",
       "      <td>0</td>\n",
       "      <td>1403</td>\n",
       "      <td>No real complaints the hotel was great great l...</td>\n",
       "      <td>105</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Duplex Double...</td>\n",
       "      <td>0 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>No Negative No real complaints the hotel was g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/31/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Rooms are nice but for elderly a bit difficult...</td>\n",
       "      <td>42</td>\n",
       "      <td>1403</td>\n",
       "      <td>Location was good and staff were ok It is cute...</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>[' Leisure trip ', ' Family with young childre...</td>\n",
       "      <td>3 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>Rooms are nice but for elderly a bit difficult...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/31/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>My room was dirty and I was afraid to walk bar...</td>\n",
       "      <td>210</td>\n",
       "      <td>1403</td>\n",
       "      <td>Great location in nice surroundings the bar an...</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>[' Leisure trip ', ' Solo traveler ', ' Duplex...</td>\n",
       "      <td>3 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>My room was dirty and I was afraid to walk bar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/24/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>You When I booked with your company on line yo...</td>\n",
       "      <td>140</td>\n",
       "      <td>1403</td>\n",
       "      <td>Amazing location and building Romantic setting</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Suite ', ' St...</td>\n",
       "      <td>10 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>You When I booked with your company on line yo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Hotel_Address  \\\n",
       "0   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "1   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "2   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "3   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "4   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "\n",
       "   Additional_Number_of_Scoring Review_Date  Average_Score   Hotel_Name  \\\n",
       "0                           194    8/3/2017            7.7  Hotel Arena   \n",
       "1                           194    8/3/2017            7.7  Hotel Arena   \n",
       "2                           194   7/31/2017            7.7  Hotel Arena   \n",
       "3                           194   7/31/2017            7.7  Hotel Arena   \n",
       "4                           194   7/24/2017            7.7  Hotel Arena   \n",
       "\n",
       "  Reviewer_Nationality                                    Negative_Review  \\\n",
       "0              Russia   I am so angry that i made this post available ...   \n",
       "1             Ireland                                         No Negative   \n",
       "2           Australia   Rooms are nice but for elderly a bit difficult...   \n",
       "3      United Kingdom   My room was dirty and I was afraid to walk bar...   \n",
       "4         New Zealand   You When I booked with your company on line yo...   \n",
       "\n",
       "   Review_Total_Negative_Word_Counts  Total_Number_of_Reviews  \\\n",
       "0                                397                     1403   \n",
       "1                                  0                     1403   \n",
       "2                                 42                     1403   \n",
       "3                                210                     1403   \n",
       "4                                140                     1403   \n",
       "\n",
       "                                     Positive_Review  \\\n",
       "0   Only the park outside of the hotel was beautiful   \n",
       "1  No real complaints the hotel was great great l...   \n",
       "2  Location was good and staff were ok It is cute...   \n",
       "3  Great location in nice surroundings the bar an...   \n",
       "4     Amazing location and building Romantic setting   \n",
       "\n",
       "   Review_Total_Positive_Word_Counts  \\\n",
       "0                                 11   \n",
       "1                                105   \n",
       "2                                 21   \n",
       "3                                 26   \n",
       "4                                  8   \n",
       "\n",
       "   Total_Number_of_Reviews_Reviewer_Has_Given  Reviewer_Score  \\\n",
       "0                                           7             2.9   \n",
       "1                                           7             7.5   \n",
       "2                                           9             7.1   \n",
       "3                                           1             3.8   \n",
       "4                                           3             6.7   \n",
       "\n",
       "                                                Tags days_since_review  \\\n",
       "0  [' Leisure trip ', ' Couple ', ' Duplex Double...            0 days   \n",
       "1  [' Leisure trip ', ' Couple ', ' Duplex Double...            0 days   \n",
       "2  [' Leisure trip ', ' Family with young childre...            3 days   \n",
       "3  [' Leisure trip ', ' Solo traveler ', ' Duplex...            3 days   \n",
       "4  [' Leisure trip ', ' Couple ', ' Suite ', ' St...           10 days   \n",
       "\n",
       "         lat       lng                                         All_Review  \n",
       "0  52.360576  4.915968  I am so angry that i made this post available ...  \n",
       "1  52.360576  4.915968  No Negative No real complaints the hotel was g...  \n",
       "2  52.360576  4.915968  Rooms are nice but for elderly a bit difficult...  \n",
       "3  52.360576  4.915968  My room was dirty and I was afraid to walk bar...  \n",
       "4  52.360576  4.915968  You When I booked with your company on line yo...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e49f1a",
   "metadata": {},
   "source": [
    "## Cleaning Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebd87403",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import nltk as nlp\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e994c05",
   "metadata": {},
   "source": [
    "def clean(seri):\n",
    "    text_list = []\n",
    "    for text in seri:\n",
    "        text = re.sub(\"[^a-zA-Z]\", \" \", text)\n",
    "        text = text.lower()\n",
    "        text = text.strip()\n",
    "        text = nltk.word_tokenize(text)\n",
    "        #text = [word for word in text if not word in set(stopwords.words(\"english\"))]\n",
    "        lemma = nlp.WordNetLemmatizer()\n",
    "        text = [lemma.lemmatize(word) for word in text]\n",
    "        text = \" \".join(text)\n",
    "        text = text.replace('  ',' ')\n",
    "        text_list.append(text)\n",
    "    return text_list        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78166855",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prerocess ve tokenize için geçen süre 515738 texts: 34.10200097958247 min\n"
     ]
    }
   ],
   "source": [
    "text_list = []\n",
    "import time\n",
    "t1 = time.time()\n",
    "for text in df_copy.All_Review:\n",
    "    text = re.sub(\"[^a-zA-Z]\", \" \", text) #we drop numbers, special characters and punctuation marks from each line.\n",
    "    text = text.lower() # lowercase\n",
    "    text = text.strip() # We drop the spaces at the beginning and end of the line.\n",
    "    text = nltk.word_tokenize(text)\n",
    "    text = [word for word in text if not word in set(stopwords.words(\"english\"))] # dropping stopwords\n",
    "    lemma = nlp.WordNetLemmatizer()\n",
    "    text = [lemma.lemmatize(word) for word in text] #\n",
    "    text = \" \".join(text)\n",
    "    text = text.replace('  ',' ')\n",
    "    text_list.append(text) # append to list\n",
    "t2 = time.time()\n",
    "print(\"prerocess ve tokenize için geçen süre\", len(df_copy), \"texts:\", (t2-t1)/60, \"min\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2c0bc67b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy[\"All_Review\"] =  text_list "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d57faa6",
   "metadata": {},
   "source": [
    "import time\n",
    "t1 = time.time()\n",
    "df_copy[\"All_Review\"]  = clean(df_copy[\"All_Review\"]) \n",
    "t2 = time.time()\n",
    "print(\"prerocess ve tokenize için geçen süre\", len(df_copy), \"texts:\", (t2-t1)/60, \"min\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0f14bbbc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Hotel_Address</th>\n",
       "      <th>Additional_Number_of_Scoring</th>\n",
       "      <th>Review_Date</th>\n",
       "      <th>Average_Score</th>\n",
       "      <th>Hotel_Name</th>\n",
       "      <th>Reviewer_Nationality</th>\n",
       "      <th>Negative_Review</th>\n",
       "      <th>Review_Total_Negative_Word_Counts</th>\n",
       "      <th>Total_Number_of_Reviews</th>\n",
       "      <th>Positive_Review</th>\n",
       "      <th>Review_Total_Positive_Word_Counts</th>\n",
       "      <th>Total_Number_of_Reviews_Reviewer_Has_Given</th>\n",
       "      <th>Reviewer_Score</th>\n",
       "      <th>Tags</th>\n",
       "      <th>days_since_review</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>All_Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>8/3/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Russia</td>\n",
       "      <td>I am so angry that i made this post available ...</td>\n",
       "      <td>397</td>\n",
       "      <td>1403</td>\n",
       "      <td>Only the park outside of the hotel was beautiful</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Duplex Double...</td>\n",
       "      <td>0 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>angry made post available via possible site us...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>8/3/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>No Negative</td>\n",
       "      <td>0</td>\n",
       "      <td>1403</td>\n",
       "      <td>No real complaints the hotel was great great l...</td>\n",
       "      <td>105</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Duplex Double...</td>\n",
       "      <td>0 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>negative real complaint hotel great great loca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/31/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Rooms are nice but for elderly a bit difficult...</td>\n",
       "      <td>42</td>\n",
       "      <td>1403</td>\n",
       "      <td>Location was good and staff were ok It is cute...</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>[' Leisure trip ', ' Family with young childre...</td>\n",
       "      <td>3 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>room nice elderly bit difficult room two story...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/31/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>My room was dirty and I was afraid to walk bar...</td>\n",
       "      <td>210</td>\n",
       "      <td>1403</td>\n",
       "      <td>Great location in nice surroundings the bar an...</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>[' Leisure trip ', ' Solo traveler ', ' Duplex...</td>\n",
       "      <td>3 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>room dirty afraid walk barefoot floor looked c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/24/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>You When I booked with your company on line yo...</td>\n",
       "      <td>140</td>\n",
       "      <td>1403</td>\n",
       "      <td>Amazing location and building Romantic setting</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Suite ', ' St...</td>\n",
       "      <td>10 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "      <td>booked company line showed picture room though...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Hotel_Address  \\\n",
       "0   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "1   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "2   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "3   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "4   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "\n",
       "   Additional_Number_of_Scoring Review_Date  Average_Score   Hotel_Name  \\\n",
       "0                           194    8/3/2017            7.7  Hotel Arena   \n",
       "1                           194    8/3/2017            7.7  Hotel Arena   \n",
       "2                           194   7/31/2017            7.7  Hotel Arena   \n",
       "3                           194   7/31/2017            7.7  Hotel Arena   \n",
       "4                           194   7/24/2017            7.7  Hotel Arena   \n",
       "\n",
       "  Reviewer_Nationality                                    Negative_Review  \\\n",
       "0              Russia   I am so angry that i made this post available ...   \n",
       "1             Ireland                                         No Negative   \n",
       "2           Australia   Rooms are nice but for elderly a bit difficult...   \n",
       "3      United Kingdom   My room was dirty and I was afraid to walk bar...   \n",
       "4         New Zealand   You When I booked with your company on line yo...   \n",
       "\n",
       "   Review_Total_Negative_Word_Counts  Total_Number_of_Reviews  \\\n",
       "0                                397                     1403   \n",
       "1                                  0                     1403   \n",
       "2                                 42                     1403   \n",
       "3                                210                     1403   \n",
       "4                                140                     1403   \n",
       "\n",
       "                                     Positive_Review  \\\n",
       "0   Only the park outside of the hotel was beautiful   \n",
       "1  No real complaints the hotel was great great l...   \n",
       "2  Location was good and staff were ok It is cute...   \n",
       "3  Great location in nice surroundings the bar an...   \n",
       "4     Amazing location and building Romantic setting   \n",
       "\n",
       "   Review_Total_Positive_Word_Counts  \\\n",
       "0                                 11   \n",
       "1                                105   \n",
       "2                                 21   \n",
       "3                                 26   \n",
       "4                                  8   \n",
       "\n",
       "   Total_Number_of_Reviews_Reviewer_Has_Given  Reviewer_Score  \\\n",
       "0                                           7             2.9   \n",
       "1                                           7             7.5   \n",
       "2                                           9             7.1   \n",
       "3                                           1             3.8   \n",
       "4                                           3             6.7   \n",
       "\n",
       "                                                Tags days_since_review  \\\n",
       "0  [' Leisure trip ', ' Couple ', ' Duplex Double...            0 days   \n",
       "1  [' Leisure trip ', ' Couple ', ' Duplex Double...            0 days   \n",
       "2  [' Leisure trip ', ' Family with young childre...            3 days   \n",
       "3  [' Leisure trip ', ' Solo traveler ', ' Duplex...            3 days   \n",
       "4  [' Leisure trip ', ' Couple ', ' Suite ', ' St...           10 days   \n",
       "\n",
       "         lat       lng                                         All_Review  \n",
       "0  52.360576  4.915968  angry made post available via possible site us...  \n",
       "1  52.360576  4.915968  negative real complaint hotel great great loca...  \n",
       "2  52.360576  4.915968  room nice elderly bit difficult room two story...  \n",
       "3  52.360576  4.915968  room dirty afraid walk barefoot floor looked c...  \n",
       "4  52.360576  4.915968  booked company line showed picture room though...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "814724c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         angry made post available via possible site us...\n",
       "1         negative real complaint hotel great great loca...\n",
       "2         room nice elderly bit difficult room two story...\n",
       "3         room dirty afraid walk barefoot floor looked c...\n",
       "4         booked company line showed picture room though...\n",
       "                                ...                        \n",
       "515733         trolly staff help take luggage room location\n",
       "515734    hotel look like surely breakfast ok got earlie...\n",
       "515735     ac useless hot week vienna gave hot air positive\n",
       "515736    negative room enormous really comfortable beli...\n",
       "515737                   rd floor work free wife staff kind\n",
       "Name: All_Review, Length: 515738, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.All_Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "383b555e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_copy[\"Reviewer_Score\"] = [0 if i <=3.0  else 1 for i in df[\"Reviewer_Score\"] ]### 3 ve altı negative review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4fb85952",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_classes(df_ratings): # create function to convert review rating into classes.\n",
    "    for i in range(len(df_ratings)):\n",
    "        if df_ratings[i]<=4.0:\n",
    "            df_ratings[i] = 0\n",
    "        elif (df_ratings[i]<=6.0) and df_ratings[i]>=5.0:\n",
    "            df_ratings[i] = 1\n",
    "        else:\n",
    "            df_ratings[i] = 2\n",
    "## If the score is 4 or less, it is negative, 5 or 6 is neatural, 7 and above is positive. \n",
    "## negative = 0, neutre = 1, positive = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "67964e3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "create_classes(df_copy[\"Reviewer_Score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "727f7940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    474447\n",
       "1.0     30479\n",
       "0.0     10812\n",
       "Name: Reviewer_Score, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy[\"Reviewer_Score\"].value_counts()# we have 474447 positive reviews, 30479 neutural review and 10812 negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "69ede104",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lst = df_copy[[\"All_Review\", \"Reviewer_Score\"]]\n",
    "df_lst.to_csv(\"All_reviews.csv\", columns = [\"All_Review\", \"Reviewer_Score\"], index = False)# savig our new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "202f3684",
   "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>All_Review</th>\n",
       "      <th>Reviewer_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>room nice elderly bit difficult room two story...</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>room dirty afraid walk barefoot floor looked c...</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>booked company line showed picture room though...</td>\n",
       "      <td>2.0</td>\n",
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      "text/plain": [
       "                                          All_Review  Reviewer_Score\n",
       "0  angry made post available via possible site us...             0.0\n",
       "1  negative real complaint hotel great great loca...             2.0\n",
       "2  room nice elderly bit difficult room two story...             2.0\n",
       "3  room dirty afraid walk barefoot floor looked c...             0.0\n",
       "4  booked company line showed picture room though...             2.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lst.head()"
   ]
  }
 ],
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