데이터합치기제출.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "ytHz6gH_1LZC",
    "outputId": "1a99654e-0f86-4feb-9035-d6cba985eaca"
   },
   "source": [
    "!pip install git+https://github.com/ssut/py-hanspell.git\n",
    "!pip install git+https://github.com/haven-jeon/PyKoSpacing.git\n",
    "!pip install konlpy\n",
    "!pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "yUsyjoOh0QzB"
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import sys\n",
    "from konlpy.tag import Okt\n",
    "from collections import Counter\n",
    "from wordcloud import WordCloud\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "OETcbTdt_dr4"
   },
   "outputs": [],
   "source": [
    "df_yanolja = pd.read_csv('야놀자한스펠.csv')\n",
    "yanolja=df_yanolja.drop('review_space',axis=1)\n",
    "yanolja=yanolja.drop('Unnamed: 0',axis=1)\n",
    "yanolja = yanolja.loc[yanolja.star>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>star</th>\n",
       "      <th>length</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>122572.000000</td>\n",
       "      <td>122572.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.631849</td>\n",
       "      <td>29.783156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.754155</td>\n",
       "      <td>45.722463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>499.000000</td>\n",
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      "text/plain": [
       "                star         length\n",
       "count  122572.000000  122572.000000\n",
       "mean        4.631849      29.783156\n",
       "std         0.754155      45.722463\n",
       "min         1.000000       1.000000\n",
       "25%         4.000000       7.000000\n",
       "50%         5.000000      14.000000\n",
       "75%         5.000000      32.000000\n",
       "max         5.000000     499.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yanolja.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:670: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  iloc._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "uhttae = pd.read_csv('여기어때전처리.csv')\n",
    "uhttae['star_yanolja'] = round(uhttae['star']/2).astype(int)\n",
    "uhttae.star_yanolja.loc[uhttae.star_yanolja == 0]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "      <th>length</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>신라스테이 역삼</td>\n",
       "      <td>8.0</td>\n",
       "      <td>출장으로 역삼 근처 호텔 찾아보다가 신라스테이 근처에 센터필드도 있고 밥집 카페도 ...</td>\n",
       "      <td>20시간 전</td>\n",
       "      <td>2022. 04. 15</td>\n",
       "      <td>195</td>\n",
       "      <td>출장으로 역삼 근처 호텔 찾아보다가 신라스테이 근처에 센터필드도 있고 밥집 카페도 ...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>신라스테이 역삼</td>\n",
       "      <td>10.0</td>\n",
       "      <td>잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬잘쉬디으잘쉬디으...</td>\n",
       "      <td>10일 전</td>\n",
       "      <td>2022. 04. 05</td>\n",
       "      <td>180</td>\n",
       "      <td>잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬잘쉬디으잘쉬디으...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>신라스테이 역삼</td>\n",
       "      <td>10.0</td>\n",
       "      <td>깔끔하고 고급스러웠습니다 가격대비 만족했던곳이라 넘넘 만족스러웠어요 로비도 깔끔하고...</td>\n",
       "      <td>12일 전</td>\n",
       "      <td>2022. 04. 03</td>\n",
       "      <td>194</td>\n",
       "      <td>깔끔하고 고급스러웠습니다 가격 대비 만족했던 곳이라 넘넘 만족스러웠어요 로비도 깔끔...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>신라스테이 역삼</td>\n",
       "      <td>10.0</td>\n",
       "      <td>직원분들 너무 친절하십니다 안내데스크가 넓어서 잠시 쉬기에도 편합니다 편의점이 바로...</td>\n",
       "      <td>24일 전</td>\n",
       "      <td>2022. 03. 22</td>\n",
       "      <td>201</td>\n",
       "      <td>직원분들 너무 친절하십니다 안내데스크가 넓어서 잠시 쉬기에도 편합니다 편의점이 바로...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>신라스테이 역삼</td>\n",
       "      <td>10.0</td>\n",
       "      <td>깔끔하고 서울에서 갑자기 일이 있거나 놀게 되었을 때 가장 합리적인 선택이라고 생각합니다</td>\n",
       "      <td>24일 전</td>\n",
       "      <td>2022. 03. 22</td>\n",
       "      <td>49</td>\n",
       "      <td>깔끔하고 서울에서 갑자기 일이 있거나 놀게 되었을 때 가장 합리적인 선택이라고 생각합니다</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>114873</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>4.3</td>\n",
       "      <td>시설 깨끗하고 다 좋은데퇴실도 안햇는데 청소하시는분인지 입구문을 맘대로 여시더라구요...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>60</td>\n",
       "      <td>시설 깨끗하고 다 좋은데 퇴실도 안 했는데 청소하시는 분인지 입구물을 맘대로 여시더...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>114874</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>10.0</td>\n",
       "      <td>깔끔하고 친철하고 모두 만족해요꼭 한번더 오고 싶은 숙박업소입니다</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>36</td>\n",
       "      <td>깔끔하고 친절하고 모두 만족해요 꼭 한 번 더 오고 싶은 숙박업소입니다</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114875</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>10.0</td>\n",
       "      <td>괜찮았어요 좋더라구요 마음에 들었어요냄새도 안나드러구요</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>30</td>\n",
       "      <td>괜찮았어요 좋더라고요 마음에 들었어요 냄새도 안나드러구요</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114876</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>10.0</td>\n",
       "      <td>시설 신림권에서 최고청결하고 수건에서 냄새도 안나고흠이라면 1000원받는 일회용품이...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>51</td>\n",
       "      <td>시설 신림권에서 최고 청결하고 수건에서 냄새도 안 나고 흠이라면 1000원 받는 일...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114877</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>9.0</td>\n",
       "      <td>일단 조식이 포함되어있어서 좋았구요 방도 깨끗하고하루정도 지내기엔 좋은거 같네요야경...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>77</td>\n",
       "      <td>일단 조식이 포함되어있어서 좋았고요 방도 깨끗하고 하루 정도 지내기엔 좋은 거 같네...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114878 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "           hotel  star                                             review  \\\n",
       "0       신라스테이 역삼   8.0  출장으로 역삼 근처 호텔 찾아보다가 신라스테이 근처에 센터필드도 있고 밥집 카페도 ...   \n",
       "1       신라스테이 역삼  10.0  잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬잘쉬디으잘쉬디으...   \n",
       "2       신라스테이 역삼  10.0  깔끔하고 고급스러웠습니다 가격대비 만족했던곳이라 넘넘 만족스러웠어요 로비도 깔끔하고...   \n",
       "3       신라스테이 역삼  10.0  직원분들 너무 친절하십니다 안내데스크가 넓어서 잠시 쉬기에도 편합니다 편의점이 바로...   \n",
       "4       신라스테이 역삼  10.0  깔끔하고 서울에서 갑자기 일이 있거나 놀게 되었을 때 가장 합리적인 선택이라고 생각합니다   \n",
       "...          ...   ...                                                ...   \n",
       "114873   뜨랑블루 호텔   4.3  시설 깨끗하고 다 좋은데퇴실도 안햇는데 청소하시는분인지 입구문을 맘대로 여시더라구요...   \n",
       "114874   뜨랑블루 호텔  10.0               깔끔하고 친철하고 모두 만족해요꼭 한번더 오고 싶은 숙박업소입니다   \n",
       "114875   뜨랑블루 호텔  10.0                     괜찮았어요 좋더라구요 마음에 들었어요냄새도 안나드러구요   \n",
       "114876   뜨랑블루 호텔  10.0  시설 신림권에서 최고청결하고 수건에서 냄새도 안나고흠이라면 1000원받는 일회용품이...   \n",
       "114877   뜨랑블루 호텔   9.0  일단 조식이 포함되어있어서 좋았구요 방도 깨끗하고하루정도 지내기엔 좋은거 같네요야경...   \n",
       "\n",
       "          date     real_date  length  \\\n",
       "0       20시간 전  2022. 04. 15     195   \n",
       "1        10일 전  2022. 04. 05     180   \n",
       "2        12일 전  2022. 04. 03     194   \n",
       "3        24일 전  2022. 03. 22     201   \n",
       "4        24일 전  2022. 03. 22      49   \n",
       "...        ...           ...     ...   \n",
       "114873    5년 전          5년 전      60   \n",
       "114874    5년 전          5년 전      36   \n",
       "114875    5년 전          5년 전      30   \n",
       "114876    5년 전          5년 전      51   \n",
       "114877    5년 전          5년 전      77   \n",
       "\n",
       "                                       review_spell_check  site  star_yanolja  \n",
       "0       출장으로 역삼 근처 호텔 찾아보다가 신라스테이 근처에 센터필드도 있고 밥집 카페도 ...     1             4  \n",
       "1       잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬잘쉬디으잘쉬디으...     1             5  \n",
       "2       깔끔하고 고급스러웠습니다 가격 대비 만족했던 곳이라 넘넘 만족스러웠어요 로비도 깔끔...     1             5  \n",
       "3       직원분들 너무 친절하십니다 안내데스크가 넓어서 잠시 쉬기에도 편합니다 편의점이 바로...     1             5  \n",
       "4       깔끔하고 서울에서 갑자기 일이 있거나 놀게 되었을 때 가장 합리적인 선택이라고 생각합니다     1             5  \n",
       "...                                                   ...   ...           ...  \n",
       "114873  시설 깨끗하고 다 좋은데 퇴실도 안 했는데 청소하시는 분인지 입구물을 맘대로 여시더...     1             2  \n",
       "114874            깔끔하고 친절하고 모두 만족해요 꼭 한 번 더 오고 싶은 숙박업소입니다     1             5  \n",
       "114875                    괜찮았어요 좋더라고요 마음에 들었어요 냄새도 안나드러구요     1             5  \n",
       "114876  시설 신림권에서 최고 청결하고 수건에서 냄새도 안 나고 흠이라면 1000원 받는 일...     1             5  \n",
       "114877  일단 조식이 포함되어있어서 좋았고요 방도 깨끗하고 하루 정도 지내기엔 좋은 거 같네...     1             4  \n",
       "\n",
       "[114878 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uhttae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VQ5cyQt3MxEl",
    "outputId": "0bf63dba-2f2b-4bb7-e0d2-7bfd426630e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 114878 entries, 0 to 114877\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   hotel               114878 non-null  object \n",
      " 1   star                114878 non-null  float64\n",
      " 2   review              114878 non-null  object \n",
      " 3   date                114878 non-null  object \n",
      " 4   real_date           114878 non-null  object \n",
      " 5   length              114878 non-null  int64  \n",
      " 6   review_spell_check  114878 non-null  object \n",
      " 7   site                114878 non-null  int64  \n",
      " 8   star_yanolja        114878 non-null  int32  \n",
      "dtypes: float64(1), int32(1), int64(2), object(5)\n",
      "memory usage: 7.4+ MB\n"
     ]
    }
   ],
   "source": [
    "uhttae.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star</th>\n",
       "      <th>length</th>\n",
       "      <th>site</th>\n",
       "      <th>star_yanolja</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>114878.000000</td>\n",
       "      <td>114878.000000</td>\n",
       "      <td>114878.0</td>\n",
       "      <td>114878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.267304</td>\n",
       "      <td>87.085926</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.639687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.455522</td>\n",
       "      <td>91.000944</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.734517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                star         length      site   star_yanolja\n",
       "count  114878.000000  114878.000000  114878.0  114878.000000\n",
       "mean        9.267304      87.085926       1.0       4.639687\n",
       "std         1.455522      91.000944       0.0       0.734517\n",
       "min         1.000000       1.000000       1.0       1.000000\n",
       "25%         9.000000      18.000000       1.0       4.000000\n",
       "50%        10.000000      44.000000       1.0       5.000000\n",
       "75%        10.000000     145.000000       1.0       5.000000\n",
       "max        10.000000     500.000000       1.0       5.000000"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "uhttae.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "uhttae['star']=uhttae['star_yanolja']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "uhttae = uhttae.drop('star_yanolja',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "yanolja['site']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "uhttae['site']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>hotel</th>\n",
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       "      <th>0</th>\n",
       "      <td>신라스테이 역삼</td>\n",
       "      <td>4</td>\n",
       "      <td>출장으로 역삼 근처 호텔 찾아보다가 신라스테이 근처에 센터필드도 있고 밥집 카페도 ...</td>\n",
       "      <td>20시간 전</td>\n",
       "      <td>2022. 04. 15</td>\n",
       "      <td>195</td>\n",
       "      <td>출장으로 역삼 근처 호텔 찾아보다가 신라스테이 근처에 센터필드도 있고 밥집 카페도 ...</td>\n",
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       "      <td>신라스테이 역삼</td>\n",
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       "      <td>잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬잘쉬디으잘쉬디으...</td>\n",
       "      <td>10일 전</td>\n",
       "      <td>2022. 04. 05</td>\n",
       "      <td>180</td>\n",
       "      <td>잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬잘쉬디으잘쉬디으...</td>\n",
       "      <td>1</td>\n",
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       "      <td>신라스테이 역삼</td>\n",
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       "      <td>12일 전</td>\n",
       "      <td>2022. 04. 03</td>\n",
       "      <td>194</td>\n",
       "      <td>깔끔하고 고급스러웠습니다 가격 대비 만족했던 곳이라 넘넘 만족스러웠어요 로비도 깔끔...</td>\n",
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       "      <td>직원분들 너무 친절하십니다 안내데스크가 넓어서 잠시 쉬기에도 편합니다 편의점이 바로...</td>\n",
       "      <td>24일 전</td>\n",
       "      <td>2022. 03. 22</td>\n",
       "      <td>201</td>\n",
       "      <td>직원분들 너무 친절하십니다 안내데스크가 넓어서 잠시 쉬기에도 편합니다 편의점이 바로...</td>\n",
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       "      <td>깔끔하고 서울에서 갑자기 일이 있거나 놀게 되었을 때 가장 합리적인 선택이라고 생각합니다</td>\n",
       "      <td>24일 전</td>\n",
       "      <td>2022. 03. 22</td>\n",
       "      <td>49</td>\n",
       "      <td>깔끔하고 서울에서 갑자기 일이 있거나 놀게 되었을 때 가장 합리적인 선택이라고 생각합니다</td>\n",
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       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
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       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>36</td>\n",
       "      <td>깔끔하고 친절하고 모두 만족해요 꼭 한 번 더 오고 싶은 숙박업소입니다</td>\n",
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       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>30</td>\n",
       "      <td>괜찮았어요 좋더라고요 마음에 들었어요 냄새도 안나드러구요</td>\n",
       "      <td>1</td>\n",
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       "      <th>114876</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>시설 신림권에서 최고청결하고 수건에서 냄새도 안나고흠이라면 1000원받는 일회용품이...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>51</td>\n",
       "      <td>시설 신림권에서 최고 청결하고 수건에서 냄새도 안 나고 흠이라면 1000원 받는 일...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>114877</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>4</td>\n",
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       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>77</td>\n",
       "      <td>일단 조식이 포함되어있어서 좋았고요 방도 깨끗하고 하루 정도 지내기엔 좋은 거 같네...</td>\n",
       "      <td>1</td>\n",
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       "<p>114878 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "           hotel  star                                             review  \\\n",
       "0       신라스테이 역삼     4  출장으로 역삼 근처 호텔 찾아보다가 신라스테이 근처에 센터필드도 있고 밥집 카페도 ...   \n",
       "1       신라스테이 역삼     5  잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬잘쉬디으잘쉬디으...   \n",
       "2       신라스테이 역삼     5  깔끔하고 고급스러웠습니다 가격대비 만족했던곳이라 넘넘 만족스러웠어요 로비도 깔끔하고...   \n",
       "3       신라스테이 역삼     5  직원분들 너무 친절하십니다 안내데스크가 넓어서 잠시 쉬기에도 편합니다 편의점이 바로...   \n",
       "4       신라스테이 역삼     5  깔끔하고 서울에서 갑자기 일이 있거나 놀게 되었을 때 가장 합리적인 선택이라고 생각합니다   \n",
       "...          ...   ...                                                ...   \n",
       "114873   뜨랑블루 호텔     2  시설 깨끗하고 다 좋은데퇴실도 안햇는데 청소하시는분인지 입구문을 맘대로 여시더라구요...   \n",
       "114874   뜨랑블루 호텔     5               깔끔하고 친철하고 모두 만족해요꼭 한번더 오고 싶은 숙박업소입니다   \n",
       "114875   뜨랑블루 호텔     5                     괜찮았어요 좋더라구요 마음에 들었어요냄새도 안나드러구요   \n",
       "114876   뜨랑블루 호텔     5  시설 신림권에서 최고청결하고 수건에서 냄새도 안나고흠이라면 1000원받는 일회용품이...   \n",
       "114877   뜨랑블루 호텔     4  일단 조식이 포함되어있어서 좋았구요 방도 깨끗하고하루정도 지내기엔 좋은거 같네요야경...   \n",
       "\n",
       "          date     real_date  length  \\\n",
       "0       20시간 전  2022. 04. 15     195   \n",
       "1        10일 전  2022. 04. 05     180   \n",
       "2        12일 전  2022. 04. 03     194   \n",
       "3        24일 전  2022. 03. 22     201   \n",
       "4        24일 전  2022. 03. 22      49   \n",
       "...        ...           ...     ...   \n",
       "114873    5년 전          5년 전      60   \n",
       "114874    5년 전          5년 전      36   \n",
       "114875    5년 전          5년 전      30   \n",
       "114876    5년 전          5년 전      51   \n",
       "114877    5년 전          5년 전      77   \n",
       "\n",
       "                                       review_spell_check  site  \n",
       "0       출장으로 역삼 근처 호텔 찾아보다가 신라스테이 근처에 센터필드도 있고 밥집 카페도 ...     1  \n",
       "1       잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬디으잘쉬잘쉬디으잘쉬디으...     1  \n",
       "2       깔끔하고 고급스러웠습니다 가격 대비 만족했던 곳이라 넘넘 만족스러웠어요 로비도 깔끔...     1  \n",
       "3       직원분들 너무 친절하십니다 안내데스크가 넓어서 잠시 쉬기에도 편합니다 편의점이 바로...     1  \n",
       "4       깔끔하고 서울에서 갑자기 일이 있거나 놀게 되었을 때 가장 합리적인 선택이라고 생각합니다     1  \n",
       "...                                                   ...   ...  \n",
       "114873  시설 깨끗하고 다 좋은데 퇴실도 안 했는데 청소하시는 분인지 입구물을 맘대로 여시더...     1  \n",
       "114874            깔끔하고 친절하고 모두 만족해요 꼭 한 번 더 오고 싶은 숙박업소입니다     1  \n",
       "114875                    괜찮았어요 좋더라고요 마음에 들었어요 냄새도 안나드러구요     1  \n",
       "114876  시설 신림권에서 최고 청결하고 수건에서 냄새도 안 나고 흠이라면 1000원 받는 일...     1  \n",
       "114877  일단 조식이 포함되어있어서 좋았고요 방도 깨끗하고 하루 정도 지내기엔 좋은 거 같네...     1  \n",
       "\n",
       "[114878 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   "source": [
    "uhttae"
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  {
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      "text/plain": [
       "                                      hotel  star  \\\n",
       "0                       AC 호텔 바이 메리어트 서울 강남     4   \n",
       "1                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "2                       AC 호텔 바이 메리어트 서울 강남     3   \n",
       "3                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "4       몬드리안 서울 이태원(Mondrian Seoul Itaewon)     5   \n",
       "...                                     ...   ...   \n",
       "122573                           그랜드 하얏트 서울     4   \n",
       "122574                           그랜드 하얏트 서울     5   \n",
       "122575                           그랜드 하얏트 서울     5   \n",
       "122576                           그랜드 하얏트 서울     5   \n",
       "122577                           그랜드 하얏트 서울     5   \n",
       "\n",
       "                                                   review          date  \\\n",
       "0                     발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요        10시간 전   \n",
       "1                                                   굳굳굳굳굳          6일 전   \n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...          6일 전   \n",
       "3       오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다 오픈초기에 사소한 부분에서...  2022. 03. 27   \n",
       "4                                        객실이 깨끗하고 너무 예뻣어요          3일 전   \n",
       "...                                                   ...           ...   \n",
       "122573                    친절하시고 조식도 맛있었어요하지만 방음이많이 안되더라구요         1시간 전   \n",
       "122574  역시 5성급 하얏트답습니다 조식 포함시 좀 비싸긴 한데 조식빼고 생각하면 매우 괜찮...         1시간 전   \n",
       "122575                                           완전 좋았습니디         6시간 전   \n",
       "122576                                          깔끔하고 좋았어용          1일 전   \n",
       "122577                                            너무좋았습니다          3일 전   \n",
       "\n",
       "           real_date  length  \\\n",
       "0       2022. 04. 03      35   \n",
       "1       2022. 04. 01       5   \n",
       "2       2022. 04. 01      84   \n",
       "3       2022. 03. 27      99   \n",
       "4       2022. 04. 01      16   \n",
       "...              ...     ...   \n",
       "122573  2022. 04. 04      31   \n",
       "122574  2022. 04. 04      89   \n",
       "122575  2022. 04. 04       8   \n",
       "122576  2022. 04. 03       9   \n",
       "122577  2022. 04. 01       7   \n",
       "\n",
       "                                       review_spell_check  site  \n",
       "0                     발레파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요     0  \n",
       "1                                                   굳굳굳굳굳     0  \n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...     0  \n",
       "3       오픈하고 얼마 안 돼서 갔었는데 깔끔하고 좋은 호텔이었습니다 오픈 초기에 사소한 부...     0  \n",
       "4                                        객실이 깨끗하고 너무 예뻤어요     0  \n",
       "...                                                   ...   ...  \n",
       "122573                  친절하시고 조식도 맛있었어요 하지만 방음이 많이 안되더라고요     0  \n",
       "122574  역시 5성급 하얏트 답습니다 조식 포함 시 좀 비싸긴 한데 조식 빼고 생각하면 매우...     0  \n",
       "122575                                           완전 좋았습니디     0  \n",
       "122576                                          깔끔하고 좋았어요     0  \n",
       "122577                                           너무 좋았습니다     0  \n",
       "\n",
       "[122572 rows x 8 columns]"
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     "execution_count": 13,
     "metadata": {},
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   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([yanolja, uhttae]) "
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  {
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   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114873</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>2</td>\n",
       "      <td>시설 깨끗하고 다 좋은데퇴실도 안햇는데 청소하시는분인지 입구문을 맘대로 여시더라구요...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>60</td>\n",
       "      <td>시설 깨끗하고 다 좋은데 퇴실도 안 했는데 청소하시는 분인지 입구물을 맘대로 여시더...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114874</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>깔끔하고 친철하고 모두 만족해요꼭 한번더 오고 싶은 숙박업소입니다</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>36</td>\n",
       "      <td>깔끔하고 친절하고 모두 만족해요 꼭 한 번 더 오고 싶은 숙박업소입니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114875</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>괜찮았어요 좋더라구요 마음에 들었어요냄새도 안나드러구요</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>30</td>\n",
       "      <td>괜찮았어요 좋더라고요 마음에 들었어요 냄새도 안나드러구요</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114876</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>시설 신림권에서 최고청결하고 수건에서 냄새도 안나고흠이라면 1000원받는 일회용품이...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>51</td>\n",
       "      <td>시설 신림권에서 최고 청결하고 수건에서 냄새도 안 나고 흠이라면 1000원 받는 일...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114877</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>4</td>\n",
       "      <td>일단 조식이 포함되어있어서 좋았구요 방도 깨끗하고하루정도 지내기엔 좋은거 같네요야경...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>77</td>\n",
       "      <td>일단 조식이 포함되어있어서 좋았고요 방도 깨끗하고 하루 정도 지내기엔 좋은 거 같네...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>237450 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      hotel  star  \\\n",
       "0                       AC 호텔 바이 메리어트 서울 강남     4   \n",
       "1                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "2                       AC 호텔 바이 메리어트 서울 강남     3   \n",
       "3                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "4       몬드리안 서울 이태원(Mondrian Seoul Itaewon)     5   \n",
       "...                                     ...   ...   \n",
       "114873                              뜨랑블루 호텔     2   \n",
       "114874                              뜨랑블루 호텔     5   \n",
       "114875                              뜨랑블루 호텔     5   \n",
       "114876                              뜨랑블루 호텔     5   \n",
       "114877                              뜨랑블루 호텔     4   \n",
       "\n",
       "                                                   review          date  \\\n",
       "0                     발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요        10시간 전   \n",
       "1                                                   굳굳굳굳굳          6일 전   \n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...          6일 전   \n",
       "3       오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다 오픈초기에 사소한 부분에서...  2022. 03. 27   \n",
       "4                                        객실이 깨끗하고 너무 예뻣어요          3일 전   \n",
       "...                                                   ...           ...   \n",
       "114873  시설 깨끗하고 다 좋은데퇴실도 안햇는데 청소하시는분인지 입구문을 맘대로 여시더라구요...          5년 전   \n",
       "114874               깔끔하고 친철하고 모두 만족해요꼭 한번더 오고 싶은 숙박업소입니다          5년 전   \n",
       "114875                     괜찮았어요 좋더라구요 마음에 들었어요냄새도 안나드러구요          5년 전   \n",
       "114876  시설 신림권에서 최고청결하고 수건에서 냄새도 안나고흠이라면 1000원받는 일회용품이...          5년 전   \n",
       "114877  일단 조식이 포함되어있어서 좋았구요 방도 깨끗하고하루정도 지내기엔 좋은거 같네요야경...          5년 전   \n",
       "\n",
       "           real_date  length  \\\n",
       "0       2022. 04. 03      35   \n",
       "1       2022. 04. 01       5   \n",
       "2       2022. 04. 01      84   \n",
       "3       2022. 03. 27      99   \n",
       "4       2022. 04. 01      16   \n",
       "...              ...     ...   \n",
       "114873          5년 전      60   \n",
       "114874          5년 전      36   \n",
       "114875          5년 전      30   \n",
       "114876          5년 전      51   \n",
       "114877          5년 전      77   \n",
       "\n",
       "                                       review_spell_check  site  \n",
       "0                     발레파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요     0  \n",
       "1                                                   굳굳굳굳굳     0  \n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...     0  \n",
       "3       오픈하고 얼마 안 돼서 갔었는데 깔끔하고 좋은 호텔이었습니다 오픈 초기에 사소한 부...     0  \n",
       "4                                        객실이 깨끗하고 너무 예뻤어요     0  \n",
       "...                                                   ...   ...  \n",
       "114873  시설 깨끗하고 다 좋은데 퇴실도 안 했는데 청소하시는 분인지 입구물을 맘대로 여시더...     1  \n",
       "114874            깔끔하고 친절하고 모두 만족해요 꼭 한 번 더 오고 싶은 숙박업소입니다     1  \n",
       "114875                    괜찮았어요 좋더라고요 마음에 들었어요 냄새도 안나드러구요     1  \n",
       "114876  시설 신림권에서 최고 청결하고 수건에서 냄새도 안 나고 흠이라면 1000원 받는 일...     1  \n",
       "114877  일단 조식이 포함되어있어서 좋았고요 방도 깨끗하고 하루 정도 지내기엔 좋은 거 같네...     1  \n",
       "\n",
       "[237450 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 합친 데이터 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"최종합친호텔리뷰.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv(\"합친호텔리뷰.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 237450 entries, 0 to 114877\n",
      "Data columns (total 8 columns):\n",
      " #   Column              Non-Null Count   Dtype \n",
      "---  ------              --------------   ----- \n",
      " 0   hotel               237450 non-null  object\n",
      " 1   star                237450 non-null  int64 \n",
      " 2   review              237450 non-null  object\n",
      " 3   date                237450 non-null  object\n",
      " 4   real_date           237450 non-null  object\n",
      " 5   length              237450 non-null  int64 \n",
      " 6   review_spell_check  237450 non-null  object\n",
      " 7   site                237450 non-null  int64 \n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 16.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>star</th>\n",
       "      <th>length</th>\n",
       "      <th>site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>266473.000000</td>\n",
       "      <td>266473.000000</td>\n",
       "      <td>266473.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.628082</td>\n",
       "      <td>63.053319</td>\n",
       "      <td>0.540021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.754286</td>\n",
       "      <td>95.389737</td>\n",
       "      <td>0.498397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>6340.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                star         length           site\n",
       "count  266473.000000  266473.000000  266473.000000\n",
       "mean        4.628082      63.053319       0.540021\n",
       "std         0.754286      95.389737       0.498397\n",
       "min         1.000000       0.000000       0.000000\n",
       "25%         4.000000      11.000000       0.000000\n",
       "50%         5.000000      24.000000       1.000000\n",
       "75%         5.000000      72.000000       1.000000\n",
       "max         5.000000    6340.000000       1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       237450\n",
       "unique         303\n",
       "top       신라스테이 구로\n",
       "freq          9261\n",
       "Name: hotel, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.hotel.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "신라스테이 구로          9261\n",
       "글래드 마포            7245\n",
       "신라스테이 마포          6651\n",
       "노보텔 앰배서더 서울 용산    5513\n",
       "롯데시티호텔 명동         5138\n",
       "                  ... \n",
       "메이원호텔                4\n",
       "사월 호텔                3\n",
       "마리호텔                 2\n",
       "종로 호텔팝 리즈 프리미어       1\n",
       "유에이치 스위트 더 서울        1\n",
       "Name: hotel, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.hotel.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j0GsXqmb9Q2P",
    "outputId": "af0567a5-6d10-4ede-a3ab-13711abd28d8",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "결측치가 존재하지 않습니다\n"
     ]
    }
   ],
   "source": [
    "# 결측치 존재 여부를 확인해주는 함수\n",
    "def check_missing_col(dataframe):\n",
    "    missing_col = []\n",
    "    counted_missing_col = 0\n",
    "    for i, col in enumerate(dataframe.columns):\n",
    "        missing_values = sum(dataframe[col].isna())\n",
    "        is_missing = True if missing_values >= 1 else False\n",
    "        if is_missing:\n",
    "            counted_missing_col += 1\n",
    "            print(f'결측치가 있는 컬럼은: {col}입니다')\n",
    "            print(f'해당 컬럼에 총 {missing_values}개의 결측치가 존재합니다.')\n",
    "            missing_col.append([col, dataframe[col].dtype])\n",
    "    if counted_missing_col == 0:\n",
    "        print('결측치가 존재하지 않습니다')\n",
    "    return missing_col\n",
    "\n",
    "missing_col = check_missing_col(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "lEIZbJleAz0C"
   },
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_Q-ltLK79oVp",
    "outputId": "51fd7779-8037-4346-ccfd-dc5dfedc8e0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    177447\n",
       "4     42599\n",
       "3     11020\n",
       "2      3658\n",
       "1      2726\n",
       "Name: star, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_count = df['star'].value_counts() # 유니크값의 개수를 확인합니다.\n",
    "val_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "tmp1Qc-3yYXS"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # 그래프 그리기 위한 패키지\n",
    "%matplotlib inline\n",
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 533
    },
    "id": "7SnKNPm1ycss",
    "outputId": "e24e0aa8-d5e9-496c-dedd-4d13454a4d41",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 히스토그램 을 사용해서 데이터의 분포를 살펴봅니다.\n",
    "feature = df['star']\n",
    "\n",
    "plt.figure(figsize=(10,7.5)) # 그래프 이미지 크기 설정\n",
    "plt.suptitle(\"Hotel review\", fontsize=30) # 부제목과 폰트 크기 설정\n",
    "\n",
    "plt.title('star', fontsize=20) # 제목과 폰트 크기 설정\n",
    "temp = feature.value_counts() # feature 변수의 변수별 개수 계산\n",
    "plt.bar(temp.keys(), temp.values, width=0.5, color='b', alpha=0.5) # 막대그래프 생성\n",
    "plt.xticks(temp.keys(), fontsize=12) # x축 값, 폰트 크기 설정\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # 레이아웃 설정\n",
    "plt.show() # 그래프 나타내기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 감성분석 (긍부정 나누기)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad = df.loc[df.star<=3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-27-04ed5c8ffe1c>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  bad['label'] = 0\n"
     ]
    }
   ],
   "source": [
    "bad['label'] = 0"
   ]
  },
  {
   "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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "      <th>length</th>\n",
       "      <th>review_spell_check</th>\n",
       "      <th>site</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>3</td>\n",
       "      <td>생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...</td>\n",
       "      <td>6일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>84</td>\n",
       "      <td>생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>3</td>\n",
       "      <td>구냥 구렇네요</td>\n",
       "      <td>2022. 02. 28</td>\n",
       "      <td>2022. 02. 28</td>\n",
       "      <td>7</td>\n",
       "      <td>구냥 고렇네요</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>3</td>\n",
       "      <td>옆방이랑 같이 자는 느낌 양 옆방이랑 방음 진짜 아예 안되고 객실 내에서는 화장실 ...</td>\n",
       "      <td>2022. 01. 23</td>\n",
       "      <td>2022. 01. 23</td>\n",
       "      <td>57</td>\n",
       "      <td>옆방이랑 같이 자는 느낌 양 옆방이랑 방음 진짜 아예 안되고 객실 내에서는 화장실 ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>1</td>\n",
       "      <td>정말 가격 5성급에 비해 방도 너무 비좁습니다그리고 방음도 안되서 윗층의 발걸음 소...</td>\n",
       "      <td>2022. 01. 09</td>\n",
       "      <td>2022. 01. 09</td>\n",
       "      <td>125</td>\n",
       "      <td>정말 가격 5성급에 비해 방도 너무 비좁습니다 그리고 방음도 안돼서 위층의 발걸음 ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>1</td>\n",
       "      <td>비싼돈 내고 미리 예약했더니 당일 예약이 가격이 더 저렴함스페리어룸9층 부터 객실 ...</td>\n",
       "      <td>2021. 12. 28</td>\n",
       "      <td>2021. 12. 28</td>\n",
       "      <td>213</td>\n",
       "      <td>비싼 돈 내고 미리 예약했더니 당일 예약이 가격이 더 저렴함 스페리 어름 9층부터 ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114712</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>1 외관상 청결함은 문제없음2 suv차량까지는 주차되는데 전화하니 안된다 된다 정확...</td>\n",
       "      <td>3년 전</td>\n",
       "      <td>3년 전</td>\n",
       "      <td>198</td>\n",
       "      <td>1 외관상 청결함은 문제없음 2 suv 차량까지는 주차되는데 전화하니 안된다 된다 ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114737</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>창문에서 웃풍이  불어 춥고 새벽 사람들 소리때문에 시끄러웠습니다</td>\n",
       "      <td>3년 전</td>\n",
       "      <td>3년 전</td>\n",
       "      <td>36</td>\n",
       "      <td>창문에서 웃풍이  불어 춥고 새벽 사람들 소리 때문에 시끄러웠습니다</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114743</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>가격대비 괜찮은거 같네요</td>\n",
       "      <td>3년 전</td>\n",
       "      <td>3년 전</td>\n",
       "      <td>13</td>\n",
       "      <td>가격 대비 괜찮은 거 같네요</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114773</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>이번이 두번째 방문인데 손가락만한 바퀴벌레가 나왔어요전화하니까 방 변경해주셔서 옮겼...</td>\n",
       "      <td>3년 전</td>\n",
       "      <td>3년 전</td>\n",
       "      <td>118</td>\n",
       "      <td>이번이 두 번째 방문인데 손가락만 한 바퀴벌레가 나왔어요 전화하니까 방 변경해주셔서...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114873</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>2</td>\n",
       "      <td>시설 깨끗하고 다 좋은데퇴실도 안햇는데 청소하시는분인지 입구문을 맘대로 여시더라구요...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>60</td>\n",
       "      <td>시설 깨끗하고 다 좋은데 퇴실도 안 했는데 청소하시는 분인지 입구물을 맘대로 여시더...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17404 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      hotel  star  \\\n",
       "2                       AC 호텔 바이 메리어트 서울 강남     3   \n",
       "61      몬드리안 서울 이태원(Mondrian Seoul Itaewon)     3   \n",
       "90      몬드리안 서울 이태원(Mondrian Seoul Itaewon)     3   \n",
       "103     몬드리안 서울 이태원(Mondrian Seoul Itaewon)     1   \n",
       "120     몬드리안 서울 이태원(Mondrian Seoul Itaewon)     1   \n",
       "...                                     ...   ...   \n",
       "114712                              뜨랑블루 호텔     3   \n",
       "114737                              뜨랑블루 호텔     3   \n",
       "114743                              뜨랑블루 호텔     3   \n",
       "114773                              뜨랑블루 호텔     3   \n",
       "114873                              뜨랑블루 호텔     2   \n",
       "\n",
       "                                                   review          date  \\\n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...          6일 전   \n",
       "61                                                구냥 구렇네요  2022. 02. 28   \n",
       "90      옆방이랑 같이 자는 느낌 양 옆방이랑 방음 진짜 아예 안되고 객실 내에서는 화장실 ...  2022. 01. 23   \n",
       "103     정말 가격 5성급에 비해 방도 너무 비좁습니다그리고 방음도 안되서 윗층의 발걸음 소...  2022. 01. 09   \n",
       "120     비싼돈 내고 미리 예약했더니 당일 예약이 가격이 더 저렴함스페리어룸9층 부터 객실 ...  2021. 12. 28   \n",
       "...                                                   ...           ...   \n",
       "114712  1 외관상 청결함은 문제없음2 suv차량까지는 주차되는데 전화하니 안된다 된다 정확...          3년 전   \n",
       "114737               창문에서 웃풍이  불어 춥고 새벽 사람들 소리때문에 시끄러웠습니다          3년 전   \n",
       "114743                                      가격대비 괜찮은거 같네요          3년 전   \n",
       "114773  이번이 두번째 방문인데 손가락만한 바퀴벌레가 나왔어요전화하니까 방 변경해주셔서 옮겼...          3년 전   \n",
       "114873  시설 깨끗하고 다 좋은데퇴실도 안햇는데 청소하시는분인지 입구문을 맘대로 여시더라구요...          5년 전   \n",
       "\n",
       "           real_date  length  \\\n",
       "2       2022. 04. 01      84   \n",
       "61      2022. 02. 28       7   \n",
       "90      2022. 01. 23      57   \n",
       "103     2022. 01. 09     125   \n",
       "120     2021. 12. 28     213   \n",
       "...              ...     ...   \n",
       "114712          3년 전     198   \n",
       "114737          3년 전      36   \n",
       "114743          3년 전      13   \n",
       "114773          3년 전     118   \n",
       "114873          5년 전      60   \n",
       "\n",
       "                                       review_spell_check  site  label  \n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...     0      0  \n",
       "61                                                구냥 고렇네요     0      0  \n",
       "90      옆방이랑 같이 자는 느낌 양 옆방이랑 방음 진짜 아예 안되고 객실 내에서는 화장실 ...     0      0  \n",
       "103     정말 가격 5성급에 비해 방도 너무 비좁습니다 그리고 방음도 안돼서 위층의 발걸음 ...     0      0  \n",
       "120     비싼 돈 내고 미리 예약했더니 당일 예약이 가격이 더 저렴함 스페리 어름 9층부터 ...     0      0  \n",
       "...                                                   ...   ...    ...  \n",
       "114712  1 외관상 청결함은 문제없음 2 suv 차량까지는 주차되는데 전화하니 안된다 된다 ...     1      0  \n",
       "114737              창문에서 웃풍이  불어 춥고 새벽 사람들 소리 때문에 시끄러웠습니다     1      0  \n",
       "114743                                    가격 대비 괜찮은 거 같네요     1      0  \n",
       "114773  이번이 두 번째 방문인데 손가락만 한 바퀴벌레가 나왔어요 전화하니까 방 변경해주셔서...     1      0  \n",
       "114873  시설 깨끗하고 다 좋은데 퇴실도 안 했는데 청소하시는 분인지 입구물을 맘대로 여시더...     1      0  \n",
       "\n",
       "[17404 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5      553\n",
       "6      340\n",
       "8      333\n",
       "7      319\n",
       "11     318\n",
       "      ... \n",
       "455      1\n",
       "458      1\n",
       "475      1\n",
       "481      1\n",
       "442      1\n",
       "Name: length, Length: 494, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad.length.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "good = df.loc[df.star==5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-8290465e9f27>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  good['label']= 1\n"
     ]
    }
   ],
   "source": [
    "good['label']= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "      <th>length</th>\n",
       "      <th>review_spell_check</th>\n",
       "      <th>site</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>5</td>\n",
       "      <td>굳굳굳굳굳</td>\n",
       "      <td>6일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>5</td>\n",
       "      <td>굳굳굳굳굳</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>5</td>\n",
       "      <td>오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다 오픈초기에 사소한 부분에서...</td>\n",
       "      <td>2022. 03. 27</td>\n",
       "      <td>2022. 03. 27</td>\n",
       "      <td>99</td>\n",
       "      <td>오픈하고 얼마 안 돼서 갔었는데 깔끔하고 좋은 호텔이었습니다 오픈 초기에 사소한 부...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>5</td>\n",
       "      <td>객실이 깨끗하고 너무 예뻣어요</td>\n",
       "      <td>3일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>16</td>\n",
       "      <td>객실이 깨끗하고 너무 예뻤어요</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>5</td>\n",
       "      <td>행복하고 즐거운시간이었습니다</td>\n",
       "      <td>4일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>15</td>\n",
       "      <td>행복하고 즐거운 시간이었습니다</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>5</td>\n",
       "      <td>너무 좋았어요 깨끗하고 좋은숙소였어요</td>\n",
       "      <td>4일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>20</td>\n",
       "      <td>너무 좋았어요 깨끗하고 좋은 숙소였어요</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114871</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>방이 깨끗하고 넓고무엇보다 침구가 너무 편안해서숙박하기에 너무 좋은 호텔입니다분위도...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>79</td>\n",
       "      <td>방이 깨끗하고 넓고 무엇보다 침구가 너무 편안해서 숙박하기에 너무 좋은 호텔입니다분...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114872</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>좋앗음</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>3</td>\n",
       "      <td>좋았음</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114874</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>깔끔하고 친철하고 모두 만족해요꼭 한번더 오고 싶은 숙박업소입니다</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>36</td>\n",
       "      <td>깔끔하고 친절하고 모두 만족해요 꼭 한 번 더 오고 싶은 숙박업소입니다</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114875</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>괜찮았어요 좋더라구요 마음에 들었어요냄새도 안나드러구요</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>30</td>\n",
       "      <td>괜찮았어요 좋더라고요 마음에 들었어요 냄새도 안나드러구요</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114876</th>\n",
       "      <td>뜨랑블루 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>시설 신림권에서 최고청결하고 수건에서 냄새도 안나고흠이라면 1000원받는 일회용품이...</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>5년 전</td>\n",
       "      <td>51</td>\n",
       "      <td>시설 신림권에서 최고 청결하고 수건에서 냄새도 안 나고 흠이라면 1000원 받는 일...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177447 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      hotel  star  \\\n",
       "1                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "3                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "4       몬드리안 서울 이태원(Mondrian Seoul Itaewon)     5   \n",
       "6       몬드리안 서울 이태원(Mondrian Seoul Itaewon)     5   \n",
       "7       몬드리안 서울 이태원(Mondrian Seoul Itaewon)     5   \n",
       "...                                     ...   ...   \n",
       "114871                              뜨랑블루 호텔     5   \n",
       "114872                              뜨랑블루 호텔     5   \n",
       "114874                              뜨랑블루 호텔     5   \n",
       "114875                              뜨랑블루 호텔     5   \n",
       "114876                              뜨랑블루 호텔     5   \n",
       "\n",
       "                                                   review          date  \\\n",
       "1                                                   굳굳굳굳굳          6일 전   \n",
       "3       오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다 오픈초기에 사소한 부분에서...  2022. 03. 27   \n",
       "4                                        객실이 깨끗하고 너무 예뻣어요          3일 전   \n",
       "6                                         행복하고 즐거운시간이었습니다          4일 전   \n",
       "7                                    너무 좋았어요 깨끗하고 좋은숙소였어요          4일 전   \n",
       "...                                                   ...           ...   \n",
       "114871  방이 깨끗하고 넓고무엇보다 침구가 너무 편안해서숙박하기에 너무 좋은 호텔입니다분위도...          5년 전   \n",
       "114872                                                좋앗음          5년 전   \n",
       "114874               깔끔하고 친철하고 모두 만족해요꼭 한번더 오고 싶은 숙박업소입니다          5년 전   \n",
       "114875                     괜찮았어요 좋더라구요 마음에 들었어요냄새도 안나드러구요          5년 전   \n",
       "114876  시설 신림권에서 최고청결하고 수건에서 냄새도 안나고흠이라면 1000원받는 일회용품이...          5년 전   \n",
       "\n",
       "           real_date  length  \\\n",
       "1       2022. 04. 01       5   \n",
       "3       2022. 03. 27      99   \n",
       "4       2022. 04. 01      16   \n",
       "6       2022. 04. 01      15   \n",
       "7       2022. 04. 01      20   \n",
       "...              ...     ...   \n",
       "114871          5년 전      79   \n",
       "114872          5년 전       3   \n",
       "114874          5년 전      36   \n",
       "114875          5년 전      30   \n",
       "114876          5년 전      51   \n",
       "\n",
       "                                       review_spell_check  site  label  \n",
       "1                                                   굳굳굳굳굳     0      1  \n",
       "3       오픈하고 얼마 안 돼서 갔었는데 깔끔하고 좋은 호텔이었습니다 오픈 초기에 사소한 부...     0      1  \n",
       "4                                        객실이 깨끗하고 너무 예뻤어요     0      1  \n",
       "6                                        행복하고 즐거운 시간이었습니다     0      1  \n",
       "7                                   너무 좋았어요 깨끗하고 좋은 숙소였어요     0      1  \n",
       "...                                                   ...   ...    ...  \n",
       "114871  방이 깨끗하고 넓고 무엇보다 침구가 너무 편안해서 숙박하기에 너무 좋은 호텔입니다분...     1      1  \n",
       "114872                                                좋았음     1      1  \n",
       "114874            깔끔하고 친절하고 모두 만족해요 꼭 한 번 더 오고 싶은 숙박업소입니다     1      1  \n",
       "114875                    괜찮았어요 좋더라고요 마음에 들었어요 냄새도 안나드러구요     1      1  \n",
       "114876  시설 신림권에서 최고 청결하고 수건에서 냄새도 안 나고 흠이라면 1000원 받는 일...     1      1  \n",
       "\n",
       "[177447 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "postive = good.sample(17400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.concat([postive, bad])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = data.label\n",
    "data = data.review_spell_check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(data, target, test_size=0.2, shuffle=True, stratify=target, random_state=777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B_cjmsBS9N6D",
    "outputId": "b2199a59-33bb-42ed-fa17-b47b46bcc625"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    17404.000000\n",
       "mean        91.972937\n",
       "std        100.765297\n",
       "min          1.000000\n",
       "25%         18.000000\n",
       "50%         52.000000\n",
       "75%        131.000000\n",
       "max        500.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hydWdFdK9Z1L",
    "outputId": "b51f5a6b-345a-4921-fd8c-6864b298ec1c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    237450.000000\n",
       "mean         57.506161\n",
       "std          76.847789\n",
       "min           1.000000\n",
       "25%          11.000000\n",
       "50%          23.000000\n",
       "75%          66.000000\n",
       "max         500.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "ytW16KsA-o_L"
   },
   "outputs": [],
   "source": [
    "df_train = df.review_spell_check.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qI5MjBIw--4Z",
    "outputId": "75982c96-e392-4a81-d759-6fbbaa7c1959"
   },
   "outputs": [],
   "source": [
    "df_train = df_train.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qg2HiHi22WEk",
    "outputId": "59df0de1-1524-43a8-ef7a-1c8755a17a52"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [발레파킹, 비용이, 있긴, 했지만, 방도, 나름, 괜찮고, 조식도, 맛있었어요]\n",
       "1                                              [굳굳굳굳굳]\n",
       "2    [생각보다, 방이, 좁아서, 놀랐어요, 아무래도, 안마의자나, 고급, 블루투스, 스...\n",
       "3    [오픈하고, 얼마, 안, 돼서, 갔었는데, 깔끔하고, 좋은, 호텔이었습니다, 오픈,...\n",
       "4                                [객실이, 깨끗하고, 너무, 예뻤어요]\n",
       "Name: review_spell_check, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_split = df['review_spell_check'].str.split()\n",
    "word_split[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "9BZ2XQx7-R5K"
   },
   "outputs": [],
   "source": [
    "def df2str(df):\n",
    "    #전체 리뷰들을 하나의 문자열로 만듦\n",
    "    s = [s for s in df]\n",
    "    document = \"\"\n",
    "    for i in range(len(s)):\n",
    "      #print(s[i])\n",
    "      document += s[i]\n",
    "    return document\n",
    "\n",
    "def get_noun(text):\n",
    "    # okt tagger를 이용해 명사 추출\n",
    "    okt = Okt()\n",
    "    noun = okt.nouns(text)\n",
    "    for i,v in enumerate(noun):\n",
    "        if len(v)<2:\n",
    "            noun.pop(i)\n",
    "    count = Counter(noun)\n",
    "    noun_list = count.most_common(100)\n",
    "\n",
    "    return noun_list\n",
    "\n",
    "def visualize(noun_list, title):\n",
    "    # 워드클라우드 이미지 생성\n",
    "    wc= WordCloud(\n",
    "        font_path = 'a고딕13.ttf',    # 한글폰트 경로 설정\n",
    "        background_color='white',                     # 배경 색깔 정하기\n",
    "        colormap = 'Dark2',                           # 폰트 색깔 정하기\n",
    "        width = 800,\n",
    "        height = 800).generate_from_frequencies(dict(noun_list)) \n",
    "    \n",
    "    plt.figure(figsize=(10,10)) #이미지 사이즈 지정\n",
    "    plt.suptitle(\"Word Cloud\", fontsize=40)\n",
    "    plt.title(title, fontsize=20)\n",
    "    plt.imshow(wc, interpolation='lanczos') #이미지의 부드럽기 정도\n",
    "    plt.axis('off') #x y 축 숫자 제거\n",
    "    plt.show()  # 워드클라우드 이미지 확인\n",
    "    \n",
    "    return wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HnE3-iHZ-XUB",
    "outputId": "92159e07-cfc7-44b3-e921-110bdba29fd3"
   },
   "outputs": [
    {
     "ename": "java.lang.OutOfMemoryError",
     "evalue": "java.lang.OutOfMemoryError: Java heap space",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mkr.lucypark.okt.OktInterface.tokenize\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.OpenKoreanTextProcessorJava.tokenize\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.OpenKoreanTextProcessor.tokenize\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.OpenKoreanTextProcessor$.tokenize\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.tokenizer.KoreanTokenizer$.tokenize\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.tokenizer.KoreanTokenizer$.tokenizeTopN\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.tokenizer.KoreanChunker$.chunk\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mscala.collection.immutable.List.flatMap\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mscala.collection.TraversableLike.flatMap$\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mscala.collection.TraversableLike.flatMap\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mscala.collection.immutable.List.foreach\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mscala.collection.TraversableLike$$Lambda$11/1846896625.apply\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mscala.collection.TraversableLike.$anonfun$flatMap$1\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.tokenizer.KoreanChunker$$$Lambda$10/1926764753.apply\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.tokenizer.KoreanChunker$.$anonfun$chunk$1\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.tokenizer.KoreanChunker$.splitChunks\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mscala.collection.immutable.List.foreach\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.tokenizer.KoreanChunker$$$Lambda$12/633070006.apply\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.tokenizer.KoreanChunker$.$anonfun$splitChunks$1$adapted\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36morg.openkoreantext.processor.tokenizer.KoreanChunker$.$anonfun$splitChunks$1\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mjava.util.regex.Pattern.matcher\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\_jpype.cp38-win_amd64.pyd\u001b[0m in \u001b[0;36mjava.util.regex.Matcher.<init>\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mException\u001b[0m: Java Exception",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mjava.lang.OutOfMemoryError\u001b[0m                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-c451666bac03>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdocument\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf2str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 전체 리뷰들을 하나의 문자열로 만듦\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnoun_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_noun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdocument\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 명사 추출, 개수 셈\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mnoun_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# 상위 20개 단어 확인\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-27-e8e70861ed4a>\u001b[0m in \u001b[0;36mget_noun\u001b[1;34m(text)\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[1;31m# okt tagger를 이용해 명사 추출\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mokt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOkt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m     \u001b[0mnoun\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mokt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnouns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnoun\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m<\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\konlpy\\tag\\_okt.py\u001b[0m in \u001b[0;36mnouns\u001b[1;34m(self, phrase)\u001b[0m\n\u001b[0;32m     81\u001b[0m         \u001b[1;34m\"\"\"Noun extractor.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m         \u001b[0mtagged\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mphrase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0ms\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtagged\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'Noun'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\konlpy\\tag\\_okt.py\u001b[0m in \u001b[0;36mpos\u001b[1;34m(self, phrase, norm, stem, join)\u001b[0m\n\u001b[0;32m     69\u001b[0m         \u001b[0mvalidate_phrase_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mphrase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m         tokens = self.jki.tokenize(\n\u001b[0m\u001b[0;32m     72\u001b[0m                     \u001b[0mphrase\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m                     \u001b[0mjpype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjava\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlang\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBoolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mjava.lang.OutOfMemoryError\u001b[0m: java.lang.OutOfMemoryError: Java heap space"
     ]
    }
   ],
   "source": [
    "document = df2str(df_train) # 전체 리뷰들을 하나의 문자열로 만듦\n",
    "noun_list = get_noun(document) # 명사 추출, 개수 셈\n",
    "noun_list[:20] # 상위 20개 단어 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "vect = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 365. GiB for an array with shape (264906, 184886) and data type float64",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-79aa89ad5d32>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mvect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\scipy\\sparse\\compressed.py\u001b[0m in \u001b[0;36mtoarray\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m   1023\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0morder\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1024\u001b[0m             \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_swap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'cf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1025\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_toarray_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1026\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_contiguous\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_contiguous\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1027\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Output array must be C or F contiguous'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\scipy\\sparse\\base.py\u001b[0m in \u001b[0;36m_process_toarray_args\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m   1183\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1184\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1185\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 365. GiB for an array with shape (264906, 184886) and data type float64"
     ]
    }
   ],
   "source": [
    "vect.fit_transform(df_train).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-04aa4f6d450a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_x\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'train_x' is not defined"
     ]
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dDVL41FeCg0t",
    "outputId": "16f2ff1a-46b0-48b1-bf86-c435952d7cca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "The following NEW packages will be installed:\n",
      "  fonts-nanum\n",
      "0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n",
      "Need to get 9,604 kB of archives.\n",
      "After this operation, 29.5 MB of additional disk space will be used.\n",
      "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-nanum all 20170925-1 [9,604 kB]\n",
      "Fetched 9,604 kB in 1s (17.0 MB/s)\n",
      "debconf: unable to initialize frontend: Dialog\n",
      "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
      "debconf: falling back to frontend: Readline\n",
      "debconf: unable to initialize frontend: Readline\n",
      "debconf: (This frontend requires a controlling tty.)\n",
      "debconf: falling back to frontend: Teletype\n",
      "dpkg-preconfigure: unable to re-open stdin: \n",
      "Selecting previously unselected package fonts-nanum.\n",
      "(Reading database ... 156210 files and directories currently installed.)\n",
      "Preparing to unpack .../fonts-nanum_20170925-1_all.deb ...\n",
      "Unpacking fonts-nanum (20170925-1) ...\n",
      "Setting up fonts-nanum (20170925-1) ...\n",
      "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n",
      "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
      "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs\n",
      "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
      "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
      "/usr/share/fonts/truetype/nanum: caching, new cache contents: 10 fonts, 0 dirs\n",
      "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
      "/root/.local/share/fonts: skipping, no such directory\n",
      "/root/.fonts: skipping, no such directory\n",
      "/var/cache/fontconfig: cleaning cache directory\n",
      "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
      "/root/.fontconfig: not cleaning non-existent cache directory\n",
      "fc-cache: succeeded\n"
     ]
    }
   ],
   "source": [
    "!sudo apt-get install -y fonts-nanum\n",
    "!sudo fc-cache -fv\n",
    "!rm ~/.cache/matplotlib -rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HlEPpIQHCrNJ",
    "outputId": "90d2f0b8-5890-4e5e-906f-57946e50c53a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting previously unselected package fonts-nanum-eco.\n",
      "(Reading database ... 156228 files and directories currently installed.)\n",
      "Preparing to unpack .../fonts-nanum-eco_1.000-6_all.deb ...\n",
      "Unpacking fonts-nanum-eco (1.000-6) ...\n",
      "Selecting previously unselected package fonts-nanum-extra.\n",
      "Preparing to unpack .../fonts-nanum-extra_20170925-1_all.deb ...\n",
      "Unpacking fonts-nanum-extra (20170925-1) ...\n",
      "Selecting previously unselected package fonts-nanum-coding.\n",
      "Preparing to unpack .../fonts-nanum-coding_2.5-1_all.deb ...\n",
      "Unpacking fonts-nanum-coding (2.5-1) ...\n",
      "Setting up fonts-nanum-extra (20170925-1) ...\n",
      "Setting up fonts-nanum-coding (2.5-1) ...\n",
      "Setting up fonts-nanum-eco (1.000-6) ...\n",
      "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n"
     ]
    }
   ],
   "source": [
    "!apt-get update -qq\n",
    "!apt-get install fonts-nanum* -qq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S2aO1ReID225"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rc('font', family='NanumBarunGothic') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "eJp-hWABCGjV",
    "outputId": "e1a3bbbd-87b9-480a-f52b-88c72728a540"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['NanumBarunGothic'] not found. Falling back to DejaVu Sans.\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 54840 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 53588 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51649 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 50896 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 50857 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 50948 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 52824 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 44061 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49892 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49884 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49444 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51221 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 47568 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 52840 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 52572 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 44256 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48169 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 47928 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "findfont: Font family ['NanumBarunGothic'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['NanumBarunGothic'] not found. Falling back to DejaVu Sans.\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 54840 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 53588 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51649 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 50896 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51060 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 50857 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 50948 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 52824 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 44061 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49892 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49884 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49444 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51221 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 47568 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 52840 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45824 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 52572 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 44256 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48169 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 47928 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('font', family='NanumBarunGothic')\n",
    "top_10 = dict(noun_list[:10]) # 추출한 명사 중 상위 10개\n",
    "plt.figure(figsize=(10,7.5))\n",
    "plt.suptitle(\"Bar Plot\", fontsize=40)\n",
    "plt.title('total reviews', fontsize=20)\n",
    "plt.bar(top_10.keys(), top_10.values(), width=0.5, color='b', alpha=0.5)\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "id": "kUKscogmEvwS",
    "outputId": "9c520ac3-bf94-47f0-dcf2-55ef8397225a"
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-1c22e924a06f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtotal_reviews\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvisualize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoun_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'total reviews'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 워드클라우드 시각화\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-15-e8e70861ed4a>\u001b[0m in \u001b[0;36mvisualize\u001b[0;34m(noun_list, title)\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0mcolormap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Dark2'\u001b[0m\u001b[0;34m,\u001b[0m                           \u001b[0;31m# 폰트 색깔 정하기\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m800\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         height = 800).generate_from_frequencies(dict(noun_list)) \n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#이미지 사이즈 지정\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/wordcloud/wordcloud.py\u001b[0m in \u001b[0;36mgenerate_from_frequencies\u001b[0;34m(self, frequencies, max_font_size)\u001b[0m\n\u001b[1;32m    424\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m                 self.generate_from_frequencies(dict(frequencies[:2]),\n\u001b[0;32m--> 426\u001b[0;31m                                                max_font_size=self.height)\n\u001b[0m\u001b[1;32m    427\u001b[0m                 \u001b[0;31m# find font sizes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m                 \u001b[0msizes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayout_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/wordcloud/wordcloud.py\u001b[0m in \u001b[0;36mgenerate_from_frequencies\u001b[0;34m(self, frequencies, max_font_size)\u001b[0m\n\u001b[1;32m    471\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m                 \u001b[0;31m# try to find a position\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m                 \u001b[0mfont\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImageFont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtruetype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfont_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfont_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    474\u001b[0m                 \u001b[0;31m# transpose font optionally\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m                 transposed_font = ImageFont.TransposedFont(\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/PIL/ImageFont.py\u001b[0m in \u001b[0;36mtruetype\u001b[0;34m(font, size, index, encoding, layout_engine)\u001b[0m\n\u001b[1;32m    646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfreetype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfont\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    649\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfont\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/PIL/ImageFont.py\u001b[0m in \u001b[0;36mfreetype\u001b[0;34m(font)\u001b[0m\n\u001b[1;32m    643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfreetype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfont\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 645\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mFreeTypeFont\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfont\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayout_engine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/PIL/ImageFont.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, font, size, index, encoding, layout_engine)\u001b[0m\n\u001b[1;32m    192\u001b[0m                     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m             self.font = core.getfont(\n\u001b[0;32m--> 194\u001b[0;31m                 \u001b[0mfont\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayout_engine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout_engine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m             )\n\u001b[1;32m    196\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: cannot open resource"
     ]
    }
   ],
   "source": [
    "total_reviews = visualize(noun_list,'total reviews') # 워드클라우드 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "oCl98qooGoG2"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer #sklearn 패키지의 CountVectorizer import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "60qNGkhJHrum"
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer() #countvectorizer 생성\n",
    "vectorizer.fit(train.review_space) # countvectorizer 학습\n",
    "X = vectorizer.transform(train.review_space) # transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "Y142nKNqI7xD"
   },
   "outputs": [],
   "source": [
    "y = train.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LJkqVu8pIRjv",
    "outputId": "25395290-5163-4542-bc22-e4c81ef47555"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.100582\n",
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      "999:\tlearn: 0.6875011\ttotal: 31m 15s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7f0b87ad91d0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "model = CatBoostClassifier()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "0Yh1ri1nvIUf"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102896    1\n",
       "17701     1\n",
       "91027     1\n",
       "5705      1\n",
       "30082     1\n",
       "         ..\n",
       "124518    0\n",
       "124525    0\n",
       "124530    0\n",
       "124534    0\n",
       "124537    0\n",
       "Name: label, Length: 19472, dtype: category\n",
       "Categories (2, int64): [0, 1]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "DjUS6p0c1_zx"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 251
    },
    "id": "Oxa4JOavJGAT",
    "outputId": "76eb279d-b895-4d40-ff71-e3ca95a89b45"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'vectorizer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-3efc31a3ea26>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#run model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mX_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvectorizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'vectorizer' is not defined"
     ]
    }
   ],
   "source": [
    "#run model\n",
    "X_pred = vectorizer.transform([input()]) \n",
    "y_pred = model.predict(X_pred)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(\"여기어때서초크롤링.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop(test.columns[0],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>star</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3607.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.806876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.878675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              star\n",
       "count  3607.000000\n",
       "mean      8.806876\n",
       "std       1.878675\n",
       "min       1.000000\n",
       "25%       8.000000\n",
       "50%      10.000000\n",
       "75%      10.000000\n",
       "max      10.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc[test.star==10,'label']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc[test.star<4,'label']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2152 entries, 0 to 3605\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   review  2152 non-null   object \n",
      " 1   star    2152 non-null   float64\n",
      " 2   label   2152 non-null   float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 67.2+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1       1.0\n",
       "2       1.0\n",
       "3       1.0\n",
       "4       1.0\n",
       "       ... \n",
       "3598    1.0\n",
       "3599    0.0\n",
       "3603    1.0\n",
       "3604    1.0\n",
       "3605    1.0\n",
       "Name: label, Length: 2152, dtype: category\n",
       "Categories (2, float64): [0.0, 1.0]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.label.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = test.loc[test.label ==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>star</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>신라스테이 서초 오랜만에 재방문했습니다.위치는 역에서 약간 떨어져있지만 너무 시끄럽...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>프론트에서 친절하게 대해주셨고, 방에 들어가서 큰침대 사이즈에 놀랐습니다. 전체적으...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>좁지만 깔끔하고 침구류가 좋아요그러나 욕실은 좁아서 불편 ㅠㅠ비지니스호텔이니 이해합니당</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>강남에서 숙박할 때마다 매번 이용합니다 한 번도 바깥이나 옆에 소리가 들린 적이 없...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>여러번 방문했는데 이번이 제일 좋았네요방 컨디션은 큰 차이 없는데 고층 배정 받았더...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3597</th>\n",
       "      <td>침구가 너무 푹신하고 일하시는 사람들이 전부 너무 친절합니다</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3598</th>\n",
       "      <td>깔끔 조식......</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>오픈한지 얼마 안되서 재빨리 예약해서 다녀왔습니다. 깔끔하고무지 마음에 들었어요. ...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>새 건물이라서 깨끗하고 좋았음</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>,신상 깔끔 조식....</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2019 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 review  star  label\n",
       "0     신라스테이 서초 오랜만에 재방문했습니다.위치는 역에서 약간 떨어져있지만 너무 시끄럽...  10.0    1.0\n",
       "1     프론트에서 친절하게 대해주셨고, 방에 들어가서 큰침대 사이즈에 놀랐습니다. 전체적으...  10.0    1.0\n",
       "2      좁지만 깔끔하고 침구류가 좋아요그러나 욕실은 좁아서 불편 ㅠㅠ비지니스호텔이니 이해합니당  10.0    1.0\n",
       "3     강남에서 숙박할 때마다 매번 이용합니다 한 번도 바깥이나 옆에 소리가 들린 적이 없...  10.0    1.0\n",
       "4     여러번 방문했는데 이번이 제일 좋았네요방 컨디션은 큰 차이 없는데 고층 배정 받았더...  10.0    1.0\n",
       "...                                                 ...   ...    ...\n",
       "3597                  침구가 너무 푹신하고 일하시는 사람들이 전부 너무 친절합니다  10.0    1.0\n",
       "3598                                        깔끔 조식......  10.0    1.0\n",
       "3603  오픈한지 얼마 안되서 재빨리 예약해서 다녀왔습니다. 깔끔하고무지 마음에 들었어요. ...  10.0    1.0\n",
       "3604                                   새 건물이라서 깨끗하고 좋았음  10.0    1.0\n",
       "3605                                      ,신상 깔끔 조식....  10.0    1.0\n",
       "\n",
       "[2019 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>star</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>신라스테이 서초 오랜만에 재방문했습니다.위치는 역에서 약간 떨어져있지만 너무 시끄럽...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>프론트에서 친절하게 대해주셨고, 방에 들어가서 큰침대 사이즈에 놀랐습니다. 전체적으...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>좁지만 깔끔하고 침구류가 좋아요그러나 욕실은 좁아서 불편 ㅠㅠ비지니스호텔이니 이해합니당</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>강남에서 숙박할 때마다 매번 이용합니다 한 번도 바깥이나 옆에 소리가 들린 적이 없...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>여러번 방문했는데 이번이 제일 좋았네요방 컨디션은 큰 차이 없는데 고층 배정 받았더...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3598</th>\n",
       "      <td>깔끔 조식......</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3599</th>\n",
       "      <td>연박하는사람이 없었을까요?첫날 발렛 만원 둘째날 부터 오천원이라고 명시해놓고출차할때...</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>오픈한지 얼마 안되서 재빨리 예약해서 다녀왔습니다. 깔끔하고무지 마음에 들었어요. ...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>새 건물이라서 깨끗하고 좋았음</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>,신상 깔끔 조식....</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2152 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 review  star  label\n",
       "0     신라스테이 서초 오랜만에 재방문했습니다.위치는 역에서 약간 떨어져있지만 너무 시끄럽...  10.0    1.0\n",
       "1     프론트에서 친절하게 대해주셨고, 방에 들어가서 큰침대 사이즈에 놀랐습니다. 전체적으...  10.0    1.0\n",
       "2      좁지만 깔끔하고 침구류가 좋아요그러나 욕실은 좁아서 불편 ㅠㅠ비지니스호텔이니 이해합니당  10.0    1.0\n",
       "3     강남에서 숙박할 때마다 매번 이용합니다 한 번도 바깥이나 옆에 소리가 들린 적이 없...  10.0    1.0\n",
       "4     여러번 방문했는데 이번이 제일 좋았네요방 컨디션은 큰 차이 없는데 고층 배정 받았더...  10.0    1.0\n",
       "...                                                 ...   ...    ...\n",
       "3598                                        깔끔 조식......  10.0    1.0\n",
       "3599  연박하는사람이 없었을까요?첫날 발렛 만원 둘째날 부터 오천원이라고 명시해놓고출차할때...   2.3    0.0\n",
       "3603  오픈한지 얼마 안되서 재빨리 예약해서 다녀왔습니다. 깔끔하고무지 마음에 들었어요. ...  10.0    1.0\n",
       "3604                                   새 건물이라서 깨끗하고 좋았음  10.0    1.0\n",
       "3605                                      ,신상 깔끔 조식....  10.0    1.0\n",
       "\n",
       "[2152 rows x 3 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test1 = test.review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test1 = test.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run model\n",
    "X_pred = vectorizer.transform(x_test1) \n",
    "y_pred = model.predict(X_pred)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9209120788824979"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7950743494423792"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_pred, y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "ghRcvDef_eKD"
   },
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "WM_CusLqC8ry"
   },
   "outputs": [],
   "source": [
    "model = Word2Vec(sentences=word_split, window=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rTxhy7wNEFg2",
    "outputId": "b77ec4b5-c7b0-4e01-a752-844dd06c38d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('직원친절도', 0.8483961224555969),\n",
       " ('시설과', 0.8474849462509155),\n",
       " ('가격', 0.8395146727561951),\n",
       " ('전망', 0.8383740186691284),\n",
       " ('면에서는', 0.8351473808288574),\n",
       " ('숙소임', 0.8311982154846191),\n",
       " ('교통', 0.8289322853088379),\n",
       " ('뷰', 0.828328013420105),\n",
       " ('편의성', 0.8215277194976807),\n",
       " ('호텔입니다', 0.8213593363761902)]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"숙소\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YGDPDyVaJEHD",
    "outputId": "8a7ae774-877c-4270-bcec-f78f95ef0a56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2643 entries, 0 to 2667\n",
      "Data columns (total 7 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   hotel               2643 non-null   object\n",
      " 1   star                2643 non-null   int64 \n",
      " 2   review              2643 non-null   object\n",
      " 3   date                2643 non-null   object\n",
      " 4   real_date           2643 non-null   object\n",
      " 5   length              2643 non-null   int64 \n",
      " 6   review_spell_check  2643 non-null   object\n",
      "dtypes: int64(2), object(5)\n",
      "memory usage: 165.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "리뷰전처리",
   "provenance": []
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
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}