0203-clustering-basic-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd4ac211",
   "metadata": {
    "id": "fd4ac211"
   },
   "source": [
    "🌱 인프런 📚 모두의 한국어 텍스트 분석과 자연어처리 with 파이썬 🐍 https://inf.run/FX4TP\n",
    "\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/corazzon/python-text-analysis/blob/main/0203-clustering-basic-output.ipynb)\n",
    "\n",
    "## 라이브러리 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf1b2e05",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29904,
     "status": "ok",
     "timestamp": 1695350631690,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "bf1b2e05",
    "outputId": "69a26819-2d89-46c4-e29b-d986cd723676"
   },
   "outputs": [],
   "source": [
    "# !pip install koreanize-matplotlib\n",
    "# !pip install pecab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "114d072c",
   "metadata": {
    "executionInfo": {
     "elapsed": 261,
     "status": "ok",
     "timestamp": 1695350643736,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "114d072c"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.cluster import KMeans\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import koreanize_matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ae922e",
   "metadata": {
    "id": "21ae922e"
   },
   "source": [
    "## 예시 텍스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "22aab1e4",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1695350644879,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "22aab1e4"
   },
   "outputs": [],
   "source": [
    "# 음식 리뷰 예제 텍스트 데이터\n",
    "corpus = [\n",
    "    \"샐러드 맛이 깔끔해서 좋아요.\",\n",
    "    \"주문한 음식이 늦게 와서 배고팠어요.\",\n",
    "    \"피자가 맛있어요.\",\n",
    "    \"라면이 너무 짜게 나왔어요.\",\n",
    "    \"배달된 치킨이 맛있어요.\",\n",
    "    \"메뉴 설명보다 양이 적어요.\",\n",
    "    \"주문한 음식이 다른 것으로 와서 다시 주문했어요.\",\n",
    "    \"맛은 좋은데 양이 좀 적은 편이에요.\",\n",
    "    \"떡볶이의 매운맛이 적절해요.\",\n",
    "    \"초밥이 신선해서 좋았어요.\",\n",
    "    \"빵이 부드럽고 맛있네요!\",\n",
    "    \"스테이크가 너무 질겼어요.\",\n",
    "    \"샐러드의 드레싱이 너무 강했어요.\",\n",
    "    \"나초의 치즈소스가 맛있었어요.\",\n",
    "    \"파스타가 약간 덜 익은 것 같아요.\",\n",
    "    \"커피가 너무 진해서 물을 더 추가했어요.\",\n",
    "    \"디저트는 달지 않아서 좋았습니다.\",\n",
    "    \"음료가 너무 달아서 다음엔 당도 조절할게요.\",\n",
    "    \"피자 도우가 너무 두꺼워서 먹기 힘들었어요.\",\n",
    "    \"햄버거의 빵이 너무 딱딱해요.\",\n",
    "    \"감자튀김이 너무 기름져서 느끼했어요.\",\n",
    "    \"치즈케이크가 아주 부드럽고 맛있어요.\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d1c334",
   "metadata": {
    "id": "d0d1c334"
   },
   "source": [
    "## 전처리\n",
    "### 형태소 분석기를 통한 조사 구두점 제거\n",
    "* https://github.com/hyunwoongko/pecab\n",
    "* 조사, 구두점 제거\n",
    "* 품사태그표 : \n",
    "    * https://konlpy.org/ko/v0.6.0/morph/\n",
    "    * https://docs.google.com/spreadsheets/d/1OGAjUvalBuX-oZvZ_-9tEfYD2gQe7hTGsgUpiiBSXI8/edit#gid=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "561fb5dd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1695350646397,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "561fb5dd",
    "outputId": "721f0430-3f88-4c3f-c02a-cb651bb66e21"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pecab._pecab.PeCab at 0x12afe7e10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pecab\n",
    "from pecab import PeCab\n",
    "\n",
    "pecab = PeCab()\n",
    "pecab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6d096918",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 253,
     "status": "ok",
     "timestamp": 1695351004568,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "6d096918",
    "outputId": "32b1206a-7356-4067-c626-d1a058aaab6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['샐러드 맛 깔끔해서 좋아요',\n",
       " '주문한 음식 늦게 와서 배고팠어요',\n",
       " '피자 맛있어요',\n",
       " '라면 너무 짜게 나왔어요',\n",
       " '배달된 치킨 맛있어요',\n",
       " '메뉴 설명 양 적어요',\n",
       " '주문한 음식 다른 것 와서 다시 주문했어요',\n",
       " '맛 좋은데 양 좀 적은 편이에요',\n",
       " '떡볶이 매운맛 적절해요',\n",
       " '초밥 신선해서 좋았어요',\n",
       " '빵 부드럽고 맛있네요',\n",
       " '스테이크 너무 질겼어요',\n",
       " '샐러드 드레싱 너무 강했어요',\n",
       " '나초 치즈소스 맛있었어요',\n",
       " '파스타 약간 덜 익은 것 같아요',\n",
       " '커피 너무 진해서 물 더 추가했어요',\n",
       " '디저트 달지 않아서 좋았습니다',\n",
       " '음료 너무 달아서 다음 당도 조절할게요',\n",
       " '피자 도우 너무 두꺼워서 먹기 힘들었어요',\n",
       " '햄버거 빵 너무 딱딱해요',\n",
       " '감자튀김 너무 기름져서 느끼했어요',\n",
       " '치즈케이크 아주 부드럽고 맛있어요']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_pecab = []\n",
    "for corpus_text in corpus:\n",
    "    join_text = []\n",
    "    for text, pos in pecab.pos(corpus_text, drop_space=False):\n",
    "        if (not pos.startswith(\"J\")) and (pos not in [\"SF\"]):\n",
    "            join_text.append(text)\n",
    "    corpus_pecab.append(\"\".join(join_text))\n",
    "corpus_pecab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8aa7023",
   "metadata": {
    "id": "d8aa7023"
   },
   "source": [
    "## 텍스트 벡터화\n",
    "* https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_extraction.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8be6019b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 312,
     "status": "ok",
     "timestamp": 1695351807188,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "8be6019b",
    "outputId": "695a468d-43b7-445e-c547-3578af72dc7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<22x67 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 82 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vectorizer\n",
    "vectorizer = TfidfVectorizer()\n",
    "dtm = vectorizer.fit_transform(corpus_pecab)\n",
    "dtm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "9ac4557e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['감자튀김', '강했어요', '같아요', '기름져서', '깔끔해서', '나왔어요', '나초', '너무', '느끼했어요',\n",
       "       '늦게', '다른', '다시', '다음', '달아서', '달지', '당도', '도우', '두꺼워서', '드레싱',\n",
       "       '디저트', '딱딱해요', '떡볶이', '라면', '맛있네요', '맛있어요', '맛있었어요', '매운맛', '먹기',\n",
       "       '메뉴', '배고팠어요', '배달된', '부드럽고', '샐러드', '설명', '스테이크', '신선해서', '아주',\n",
       "       '않아서', '약간', '와서', '음료', '음식', '익은', '적어요', '적은', '적절해요', '조절할게요',\n",
       "       '좋아요', '좋았습니다', '좋았어요', '좋은데', '주문한', '주문했어요', '진해서', '질겼어요', '짜게',\n",
       "       '초밥', '추가했어요', '치즈소스', '치즈케이크', '치킨', '커피', '파스타', '편이에요', '피자',\n",
       "       '햄버거', '힘들었어요'], dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c4159275",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 360
    },
    "executionInfo": {
     "elapsed": 1217,
     "status": "ok",
     "timestamp": 1695351809587,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "c4159275",
    "outputId": "020e10b9-59d2-4b48-d206-a5c0cde913a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 67)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dtm = pd.DataFrame(dtm.toarray(), columns=vectorizer.get_feature_names_out())\n",
    "df_dtm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e604c00",
   "metadata": {
    "id": "5e604c00"
   },
   "source": [
    "## 군집화\n",
    "\n",
    "* https://scikit-learn.org/stable/modules/clustering.html#k-means\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ec808348",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 355,
     "status": "ok",
     "timestamp": 1695351811214,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "ec808348",
    "outputId": "c5d3378d-2a19-4b10-8502-7844642663f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 5, 1, 5, 1, 2, 6, 0, 1, 4, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 4],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K-means 군집화\n",
    "num_clusters = 7 \n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=num_clusters, n_init=\"auto\", random_state=42)\n",
    "clusters = kmeans.fit(df_dtm).predict(df_dtm)\n",
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "20e370b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 5, 1, 5, 1, 2, 6, 0, 1, 4, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 4],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# labels_\n",
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9bf7515",
   "metadata": {
    "id": "c9bf7515"
   },
   "source": [
    "## 군집화 결과\n",
    "* 같은 내용의 텍스트끼리 군집화가 되었는지 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "60e97c10",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 613
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1695351813541,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "60e97c10",
    "outputId": "8be06355-e93d-4db5-f2ca-1411971beca5"
   },
   "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>corpus</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>떡볶이의 매운맛이 적절해요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>파스타가 약간 덜 익은 것 같아요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>나초의 치즈소스가 맛있었어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>샐러드의 드레싱이 너무 강했어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>스테이크가 너무 질겼어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>감자튀김이 너무 기름져서 느끼했어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>초밥이 신선해서 좋았어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>커피가 너무 진해서 물을 더 추가했어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>샐러드 맛이 깔끔해서 좋아요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>메뉴 설명보다 양이 적어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>라면이 너무 짜게 나왔어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>햄버거의 빵이 너무 딱딱해요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>음료가 너무 달아서 다음엔 당도 조절할게요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>디저트는 달지 않아서 좋았습니다.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>주문한 음식이 다른 것으로 와서 다시 주문했어요.</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>주문한 음식이 늦게 와서 배고팠어요.</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>피자 도우가 너무 두꺼워서 먹기 힘들었어요.</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>빵이 부드럽고 맛있네요!</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>치즈케이크가 아주 부드럽고 맛있어요.</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>배달된 치킨이 맛있어요.</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>피자가 맛있어요.</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>맛은 좋은데 양이 좀 적은 편이에요.</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         corpus  label\n",
       "8               떡볶이의 매운맛이 적절해요.      0\n",
       "14          파스타가 약간 덜 익은 것 같아요.      1\n",
       "13             나초의 치즈소스가 맛있었어요.      1\n",
       "12           샐러드의 드레싱이 너무 강했어요.      1\n",
       "11               스테이크가 너무 질겼어요.      1\n",
       "20         감자튀김이 너무 기름져서 느끼했어요.      1\n",
       "9                초밥이 신선해서 좋았어요.      1\n",
       "15       커피가 너무 진해서 물을 더 추가했어요.      1\n",
       "0              샐러드 맛이 깔끔해서 좋아요.      1\n",
       "5               메뉴 설명보다 양이 적어요.      1\n",
       "3               라면이 너무 짜게 나왔어요.      1\n",
       "19             햄버거의 빵이 너무 딱딱해요.      1\n",
       "17     음료가 너무 달아서 다음엔 당도 조절할게요.      1\n",
       "16           디저트는 달지 않아서 좋았습니다.      1\n",
       "6   주문한 음식이 다른 것으로 와서 다시 주문했어요.      2\n",
       "1          주문한 음식이 늦게 와서 배고팠어요.      2\n",
       "18     피자 도우가 너무 두꺼워서 먹기 힘들었어요.      3\n",
       "10                빵이 부드럽고 맛있네요!      4\n",
       "21         치즈케이크가 아주 부드럽고 맛있어요.      4\n",
       "4                 배달된 치킨이 맛있어요.      5\n",
       "2                     피자가 맛있어요.      5\n",
       "7          맛은 좋은데 양이 좀 적은 편이에요.      6"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df[\"corpus\"] = corpus\n",
    "df[\"label\"] = kmeans.labels_\n",
    "df.sort_values(\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "29bf9727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "1    13\n",
       "2     2\n",
       "5     2\n",
       "4     2\n",
       "6     1\n",
       "0     1\n",
       "3     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd09f7ae",
   "metadata": {},
   "source": [
    "### 차원축소와 시각화\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html\n",
    "* 실제 텍스트와 label_ 값이 비슷한 의미의 텍스트끼리 군집화 되었는지 차원 축소기법을 통해 2차원 평면에 표현해 봅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d1d4ac15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 67)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dtm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "ce30f7d5",
   "metadata": {
    "executionInfo": {
     "elapsed": 386,
     "status": "ok",
     "timestamp": 1695351744417,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "ce30f7d5"
   },
   "outputs": [],
   "source": [
    "# t-SNE (t-distributed Stochastic Neighbor Embedding)\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# help(TSNE)\n",
    "tsne = TSNE(n_components=2, perplexity=10, random_state=42)\n",
    "tsne_result = tsne.fit_transform(df_dtm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7cd23d81",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>corpus</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.981525</td>\n",
       "      <td>3.346380</td>\n",
       "      <td>샐러드 맛이 깔끔해서 좋아요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.025337</td>\n",
       "      <td>-9.345027</td>\n",
       "      <td>주문한 음식이 늦게 와서 배고팠어요.</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-14.553224</td>\n",
       "      <td>-0.477301</td>\n",
       "      <td>피자가 맛있어요.</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.886031</td>\n",
       "      <td>19.599838</td>\n",
       "      <td>라면이 너무 짜게 나왔어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-18.636333</td>\n",
       "      <td>-2.218875</td>\n",
       "      <td>배달된 치킨이 맛있어요.</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-38.751343</td>\n",
       "      <td>-3.634021</td>\n",
       "      <td>메뉴 설명보다 양이 적어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.398985</td>\n",
       "      <td>-8.017451</td>\n",
       "      <td>주문한 음식이 다른 것으로 와서 다시 주문했어요.</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-7.759202</td>\n",
       "      <td>-29.874292</td>\n",
       "      <td>맛은 좋은데 양이 좀 적은 편이에요.</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23.096228</td>\n",
       "      <td>-7.471128</td>\n",
       "      <td>떡볶이의 매운맛이 적절해요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-33.593315</td>\n",
       "      <td>20.094219</td>\n",
       "      <td>초밥이 신선해서 좋았어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-11.496445</td>\n",
       "      <td>-10.310987</td>\n",
       "      <td>빵이 부드럽고 맛있네요!</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-2.864507</td>\n",
       "      <td>13.399057</td>\n",
       "      <td>스테이크가 너무 질겼어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.020275</td>\n",
       "      <td>6.712429</td>\n",
       "      <td>샐러드의 드레싱이 너무 강했어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-27.980890</td>\n",
       "      <td>-23.905991</td>\n",
       "      <td>나초의 치즈소스가 맛있었어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.381809</td>\n",
       "      <td>-25.949480</td>\n",
       "      <td>파스타가 약간 덜 익은 것 같아요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-6.857045</td>\n",
       "      <td>10.008870</td>\n",
       "      <td>커피가 너무 진해서 물을 더 추가했어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25.070839</td>\n",
       "      <td>14.880673</td>\n",
       "      <td>디저트는 달지 않아서 좋았습니다.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.669381</td>\n",
       "      <td>15.031615</td>\n",
       "      <td>음료가 너무 달아서 다음엔 당도 조절할게요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-11.961347</td>\n",
       "      <td>3.754031</td>\n",
       "      <td>피자 도우가 너무 두꺼워서 먹기 힘들었어요.</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-6.634336</td>\n",
       "      <td>16.073687</td>\n",
       "      <td>햄버거의 빵이 너무 딱딱해요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-11.396762</td>\n",
       "      <td>14.342366</td>\n",
       "      <td>감자튀김이 너무 기름져서 느끼했어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-13.382157</td>\n",
       "      <td>-6.593828</td>\n",
       "      <td>치즈케이크가 아주 부드럽고 맛있어요.</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            x          y                       corpus  label\n",
       "0    1.981525   3.346380             샐러드 맛이 깔끔해서 좋아요.      1\n",
       "1    0.025337  -9.345027         주문한 음식이 늦게 와서 배고팠어요.      2\n",
       "2  -14.553224  -0.477301                    피자가 맛있어요.      5\n",
       "3   -2.886031  19.599838              라면이 너무 짜게 나왔어요.      1\n",
       "4  -18.636333  -2.218875                배달된 치킨이 맛있어요.      5\n",
       "5  -38.751343  -3.634021              메뉴 설명보다 양이 적어요.      1\n",
       "6    3.398985  -8.017451  주문한 음식이 다른 것으로 와서 다시 주문했어요.      2\n",
       "7   -7.759202 -29.874292         맛은 좋은데 양이 좀 적은 편이에요.      6\n",
       "8   23.096228  -7.471128              떡볶이의 매운맛이 적절해요.      0\n",
       "9  -33.593315  20.094219               초밥이 신선해서 좋았어요.      1\n",
       "10 -11.496445 -10.310987                빵이 부드럽고 맛있네요!      4\n",
       "11  -2.864507  13.399057               스테이크가 너무 질겼어요.      1\n",
       "12   0.020275   6.712429           샐러드의 드레싱이 너무 강했어요.      1\n",
       "13 -27.980890 -23.905991             나초의 치즈소스가 맛있었어요.      1\n",
       "14  12.381809 -25.949480          파스타가 약간 덜 익은 것 같아요.      1\n",
       "15  -6.857045  10.008870       커피가 너무 진해서 물을 더 추가했어요.      1\n",
       "16  25.070839  14.880673           디저트는 달지 않아서 좋았습니다.      1\n",
       "17   2.669381  15.031615     음료가 너무 달아서 다음엔 당도 조절할게요.      1\n",
       "18 -11.961347   3.754031     피자 도우가 너무 두꺼워서 먹기 힘들었어요.      3\n",
       "19  -6.634336  16.073687             햄버거의 빵이 너무 딱딱해요.      1\n",
       "20 -11.396762  14.342366         감자튀김이 너무 기름져서 느끼했어요.      1\n",
       "21 -13.382157  -6.593828         치즈케이크가 아주 부드럽고 맛있어요.      4"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tsne = pd.DataFrame(tsne_result, columns=[\"x\", \"y\"])\n",
    "df_tsne[\"corpus\"] = corpus\n",
    "df_tsne[\"label\"] = kmeans.labels_\n",
    "df_tsne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "0284a304",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.colormaps()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "6faf05a5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "executionInfo": {
     "elapsed": 441,
     "status": "ok",
     "timestamp": 1695351745858,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "6faf05a5",
    "outputId": "58d912f3-fc25-437b-8e9f-1ce999b41b39"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2차원 산점도로 군집 결과 시각화\n",
    "sns.scatterplot(data=df_tsne, x=\"x\", y=\"y\", hue=\"label\", palette=\"Set1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "61180ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.text?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "5d65c7b3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 851
    },
    "executionInfo": {
     "elapsed": 1310,
     "status": "ok",
     "timestamp": 1695351748392,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "5d65c7b3",
    "outputId": "fb88ac41-c888-40b6-c13a-b50b162da655"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 어떤 텍스트인지 표기하기\n",
    "plt.figure(figsize=(15, 8))\n",
    "sns.scatterplot(data=df_tsne, x=\"x\", y=\"y\", hue=\"label\", palette=\"Set1\")\n",
    "for i in df_tsne.index:\n",
    "    plt.text(x=df_tsne.loc[i, \"x\"], y=df_tsne.loc[i, \"y\"], s=df_tsne.loc[i, \"corpus\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7809c85c",
   "metadata": {
    "id": "7809c85c"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26acf191",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ea44ae6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66a37982",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a97090f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56d81d21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2f8c527",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [
    {
     "file_id": "1r2pMk14ED01xYSOFKjYyVE7Gw1Gy0d7H",
     "timestamp": 1695350447401
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": false,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}