_2_키워드_분석(Keyword_Analysis).ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yoonjong8739/NLP_study/blob/master/_2_%ED%82%A4%EC%9B%8C%EB%93%9C_%EB%B6%84%EC%84%9D(Keyword_Analysis).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njXpmXifM6D-"
      },
      "source": [
        "# 키워드 분석(Keyword Analysis)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pvUu6DwlNLm9"
      },
      "source": [
        "* 핵심어(keyword)란 텍스트 자료의 중요한 내용을 압축적으로 제시하는 단어 또는 문구\n",
        "* 핵심어 분석이란 불용어 제거와 어간추출 및 형태소 분석 등의 자연어 처리를 시행한 후 텍스트에서 많이 등장하는 형태소의 등장 빈도를 분석함으로써 핵심어를 추출   \n",
        "* 특정 텍스트 자료에 많이 나타나는 형태소가 그 텍스트 주제를 표출할 가능성이 높다는 가정에 기초  \n",
        "* 물론 빈도 분석에서 영어의 전치사나 한국어의 조사와 같이 의미를 별로 담고 있지 않은 불용어는 제외하는 것이 좋음\n",
        "* 키워드 분석은 텍스트의 주제 추정, 텍스트 유사도, 검색 엔진의 검색 결과 우선 순위 측정 등 다양하게 사용될 수 있음"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "armZiPJzmIFk"
      },
      "source": [
        "## 한글 폰트 설정\n",
        "\n",
        "* 실행 후 런타임 재시작 필요"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDd_QGuRmGe5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "779d9019-d60f-436c-f70b-5208bd100eed"
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "!apt -qq -y install fonts-nanum\n",
        "\n",
        "import matplotlib.font_manager as fm\n",
        "fe = fm.FontEntry(\n",
        "    fname=r'/usr/share/fonts/truetype/nanum/NanumGothic.ttf', # ttf 파일이 저장되어 있는 경로\n",
        "    name='NanumGothic')                        # 이 폰트의 원하는 이름 설정\n",
        "fm.fontManager.ttflist.insert(0, fe)              # Matplotlib에 폰트 추가\n",
        "\n",
        "plt.rcParams.update({'font.size': 10, 'font.family': 'NanumGothic'}) # 폰트 설정"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fonts-nanum is already the newest version (20200506-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 8 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCfqJW-emM1D"
      },
      "source": [
        "## 한국어 자연어 처리 konlpy와 형태소 분석기 MeCab 설치\n",
        "\n",
        "* https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtLshLTaV3sR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0553ca1b-c0e5-4379-fe00-1c1e449f0e06"
      },
      "source": [
        "!set -x \\\n",
        "&& pip install konlpy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+ pip install konlpy\n",
            "Requirement already satisfied: konlpy in /usr/local/lib/python3.10/dist-packages (0.6.0)\n",
            "Requirement already satisfied: JPype1>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from konlpy) (1.4.1)\n",
            "Requirement already satisfied: lxml>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from konlpy) (4.9.3)\n",
            "Requirement already satisfied: numpy>=1.6 in /usr/local/lib/python3.10/dist-packages (from konlpy) (1.22.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from JPype1>=0.7.0->konlpy) (23.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrJWPH28S2Bp"
      },
      "source": [
        "## 네이버 영화 리뷰 데이터"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XB2wgChQ8KV"
      },
      "source": [
        "* 키워드 분석에는 네이버 영화 리뷰 데이터를 사용\n",
        "* 데이터 다운로드: https://raw.githubusercontent.com/e9t/nsmc/master/ratings.txt"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f87bl27DQS87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a64148e-818d-4cde-ead6-249733176d80"
      },
      "source": [
        "import urllib.request\n",
        "\n",
        "raw = urllib.request.urlopen('https://raw.githubusercontent.com/e9t/nsmc/master/ratings.txt').readlines()\n",
        "print(raw[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[b'id\\tdocument\\tlabel\\n', b'8112052\\t\\xec\\x96\\xb4\\xeb\\xa6\\xb4\\xeb\\x95\\x8c\\xeb\\xb3\\xb4\\xea\\xb3\\xa0 \\xec\\xa7\\x80\\xea\\xb8\\x88\\xeb\\x8b\\xa4\\xec\\x8b\\x9c\\xeb\\xb4\\x90\\xeb\\x8f\\x84 \\xec\\x9e\\xac\\xeb\\xb0\\x8c\\xec\\x96\\xb4\\xec\\x9a\\x94\\xe3\\x85\\x8b\\xe3\\x85\\x8b\\t1\\n', b'8132799\\t\\xeb\\x94\\x94\\xec\\x9e\\x90\\xec\\x9d\\xb8\\xec\\x9d\\x84 \\xeb\\xb0\\xb0\\xec\\x9a\\xb0\\xeb\\x8a\\x94 \\xed\\x95\\x99\\xec\\x83\\x9d\\xec\\x9c\\xbc\\xeb\\xa1\\x9c, \\xec\\x99\\xb8\\xea\\xb5\\xad\\xeb\\x94\\x94\\xec\\x9e\\x90\\xec\\x9d\\xb4\\xeb\\x84\\x88\\xec\\x99\\x80 \\xea\\xb7\\xb8\\xeb\\x93\\xa4\\xec\\x9d\\xb4 \\xec\\x9d\\xbc\\xea\\xb5\\xb0 \\xec\\xa0\\x84\\xed\\x86\\xb5\\xec\\x9d\\x84 \\xed\\x86\\xb5\\xed\\x95\\xb4 \\xeb\\xb0\\x9c\\xec\\xa0\\x84\\xed\\x95\\xb4\\xea\\xb0\\x80\\xeb\\x8a\\x94 \\xeb\\xac\\xb8\\xed\\x99\\x94\\xec\\x82\\xb0\\xec\\x97\\x85\\xec\\x9d\\xb4 \\xeb\\xb6\\x80\\xeb\\x9f\\xac\\xec\\x9b\\xa0\\xeb\\x8a\\x94\\xeb\\x8d\\xb0. \\xec\\x82\\xac\\xec\\x8b\\xa4 \\xec\\x9a\\xb0\\xeb\\xa6\\xac\\xeb\\x82\\x98\\xeb\\x9d\\xbc\\xec\\x97\\x90\\xec\\x84\\x9c\\xeb\\x8f\\x84 \\xea\\xb7\\xb8 \\xec\\x96\\xb4\\xeb\\xa0\\xa4\\xec\\x9a\\xb4\\xec\\x8b\\x9c\\xec\\xa0\\x88\\xec\\x97\\x90 \\xeb\\x81\\x9d\\xea\\xb9\\x8c\\xec\\xa7\\x80 \\xec\\x97\\xb4\\xec\\xa0\\x95\\xec\\x9d\\x84 \\xec\\xa7\\x80\\xed\\x82\\xa8 \\xeb\\x85\\xb8\\xeb\\x9d\\xbc\\xeb\\x85\\xb8 \\xea\\xb0\\x99\\xec\\x9d\\x80 \\xec\\xa0\\x84\\xed\\x86\\xb5\\xec\\x9d\\xb4\\xec\\x9e\\x88\\xec\\x96\\xb4 \\xec\\xa0\\x80\\xec\\x99\\x80 \\xea\\xb0\\x99\\xec\\x9d\\x80 \\xec\\x82\\xac\\xeb\\x9e\\x8c\\xeb\\x93\\xa4\\xec\\x9d\\xb4 \\xea\\xbf\\x88\\xec\\x9d\\x84 \\xea\\xbe\\xb8\\xea\\xb3\\xa0 \\xec\\x9d\\xb4\\xeb\\xa4\\x84\\xeb\\x82\\x98\\xea\\xb0\\x88 \\xec\\x88\\x98 \\xec\\x9e\\x88\\xeb\\x8b\\xa4\\xeb\\x8a\\x94 \\xea\\xb2\\x83\\xec\\x97\\x90 \\xea\\xb0\\x90\\xec\\x82\\xac\\xed\\x95\\xa9\\xeb\\x8b\\x88\\xeb\\x8b\\xa4.\\t1\\n', b'4655635\\t\\xed\\x8f\\xb4\\xeb\\xa6\\xac\\xec\\x8a\\xa4\\xec\\x8a\\xa4\\xed\\x86\\xa0\\xeb\\xa6\\xac \\xec\\x8b\\x9c\\xeb\\xa6\\xac\\xec\\xa6\\x88\\xeb\\x8a\\x94 1\\xeb\\xb6\\x80\\xed\\x84\\xb0 \\xeb\\x89\\xb4\\xea\\xb9\\x8c\\xec\\xa7\\x80 \\xeb\\xb2\\x84\\xeb\\xa6\\xb4\\xea\\xbb\\x98 \\xed\\x95\\x98\\xeb\\x82\\x98\\xeb\\x8f\\x84 \\xec\\x97\\x86\\xec\\x9d\\x8c.. \\xec\\xb5\\x9c\\xea\\xb3\\xa0.\\t1\\n', b'9251303\\t\\xec\\x99\\x80.. \\xec\\x97\\xb0\\xea\\xb8\\xb0\\xea\\xb0\\x80 \\xec\\xa7\\x84\\xec\\xa7\\x9c \\xea\\xb0\\x9c\\xec\\xa9\\x94\\xea\\xb5\\xac\\xeb\\x82\\x98.. \\xec\\xa7\\x80\\xeb\\xa3\\xa8\\xed\\x95\\xa0\\xea\\xb1\\xb0\\xeb\\x9d\\xbc\\xea\\xb3\\xa0 \\xec\\x83\\x9d\\xea\\xb0\\x81\\xed\\x96\\x88\\xeb\\x8a\\x94\\xeb\\x8d\\xb0 \\xeb\\xaa\\xb0\\xec\\x9e\\x85\\xed\\x95\\xb4\\xec\\x84\\x9c \\xeb\\xb4\\xa4\\xeb\\x8b\\xa4.. \\xea\\xb7\\xb8\\xeb\\x9e\\x98 \\xec\\x9d\\xb4\\xeb\\x9f\\xb0\\xea\\xb2\\x8c \\xec\\xa7\\x84\\xec\\xa7\\x9c \\xec\\x98\\x81\\xed\\x99\\x94\\xec\\xa7\\x80\\t1\\n']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRaviObSRI-7"
      },
      "source": [
        "* 데이터를 확인해보면 모두 바이트로 이루어져 있으며, 리뷰 외의 다른 데이터도 존재\n",
        "* 데이터를 문자열로 변환하고 리뷰만 추출"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIDt6COKRJGO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7303cc8-8891-4208-ec40-bdb6601d8926"
      },
      "source": [
        "raw = [x.decode() for x in raw[1:] ]\n",
        "\n",
        "reviews = []\n",
        "for i in raw:\n",
        "    reviews.append(i.split('\\t')[1])\n",
        "\n",
        "print(reviews[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['어릴때보고 지금다시봐도 재밌어요ㅋㅋ', '디자인을 배우는 학생으로, 외국디자이너와 그들이 일군 전통을 통해 발전해가는 문화산업이 부러웠는데. 사실 우리나라에서도 그 어려운시절에 끝까지 열정을 지킨 노라노 같은 전통이있어 저와 같은 사람들이 꿈을 꾸고 이뤄나갈 수 있다는 것에 감사합니다.', '폴리스스토리 시리즈는 1부터 뉴까지 버릴께 하나도 없음.. 최고.', '와.. 연기가 진짜 개쩔구나.. 지루할거라고 생각했는데 몰입해서 봤다.. 그래 이런게 진짜 영화지', '안개 자욱한 밤하늘에 떠 있는 초승달 같은 영화.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65sLf2AkL440"
      },
      "source": [
        "## 형태소 분석을 이용한 명사 추출\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RvU-xVmf-aQ"
      },
      "source": [
        "* 형태소 분석기 Mecab으로 명사만 추출"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kC0wR2GBTD4G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70ed16af-16b6-4d45-f12f-ffe85f7d86a0"
      },
      "source": [
        "from konlpy.tag import Okt\n",
        "tagger = Okt()\n",
        "\n",
        "nouns = []\n",
        "for review in reviews:\n",
        "    for noun in tagger.nouns(review):\n",
        "        nouns.append(noun)\n",
        "\n",
        "nouns[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['때', '보고', '지금', '다시', '디자인', '학생', '외국', '디자이너', '그', '일군']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RfG7OgV_L446"
      },
      "source": [
        "### 불용어(Stopwords) 사전 만들기\n",
        "\n",
        "* 형태소 분석을 통해 조사, 접속사 등의 제거 가능\n",
        "* 하지만 한국어는 명사에서도 상당히 많은 불필요한 단어들이 포함\n",
        "* 사용자가 직접 불용어 사전을 유지하면서 불필요한 단어 제거 필요\n",
        "* 불용어 예: `전 난 일 걸 뭐 줄 만 건 분 개 끝 잼 이거 번 중 듯 때 게 내 말 나 수 거 점 것`\n",
        "* 빈도가 너무 커서 분석에 방해되는 단어도 제거 필요 (예: `영화`)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PxXHMeHEL446",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e104d1e-91e5-47cd-dc3b-735032eb3646"
      },
      "source": [
        "stop_words = \"영화 전 난 일 걸 뭐 줄 만 건 분 개 끝 잼 이거 번 중 듯 때 게 내 말 나 수 거 점 것 이 그 다시 지금\"\n",
        "stop_words = stop_words.split(\" \")\n",
        "print(stop_words)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['영화', '전', '난', '일', '걸', '뭐', '줄', '만', '건', '분', '개', '끝', '잼', '이거', '번', '중', '듯', '때', '게', '내', '말', '나', '수', '거', '점', '것', '이', '그', '다시', '지금']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHBAWtzAL449"
      },
      "source": [
        "### 불용어를 제외하여 형태소 분석 수행\n",
        "\n",
        "* 한글 텍스트에 대해서 형태소 분석 수행\n",
        "* 분석으로 추출하는 명사 중에서 불용어에 포함되지 않은 텍스트만 추출하여 저장"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oz2yf12vL44-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15b4c69e-252f-475f-81f5-75801a7e3a17"
      },
      "source": [
        "from konlpy.tag import Okt\n",
        "tagger = Okt()\n",
        "\n",
        "nouns = [noun for review in reviews for noun in tagger.nouns(review) if noun not in stop_words]\n",
        "print(nouns[:10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['보고', '디자인', '학생', '외국', '디자이너', '일군', '전통', '통해', '발전', '문화']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHRt9alfTMj3"
      },
      "source": [
        "## 단어 빈도수 측정"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXbsTUQLA7FH"
      },
      "source": [
        "* 단어 빈도수 측정에는 `collections` 라이브러리의 `Counter` 함수를 이용\n",
        "* `collections` 라이브러리는 내장 라이브러리로 별도 설치가 필요없음\n",
        "* `counter`를 이용하면 각 단어와 각 단어의 빈도 수를 딕셔너리로 편리하게 생성 가능"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nnOU6NDTWM9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc1c8242-d7f9-422f-9ae5-9c4106c9f4c4"
      },
      "source": [
        "from collections import Counter\n",
        "\n",
        "nouns_counter = Counter(nouns)\n",
        "top_nouns = dict(nouns_counter.most_common(50))\n",
        "top_nouns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'정말': 12843,\n",
              " '진짜': 11279,\n",
              " '연기': 8631,\n",
              " '평점': 8550,\n",
              " '최고': 8049,\n",
              " '생각': 7138,\n",
              " '스토리': 7045,\n",
              " '왜': 6886,\n",
              " '드라마': 6710,\n",
              " '감동': 6513,\n",
              " '사람': 6479,\n",
              " '보고': 6121,\n",
              " '더': 5886,\n",
              " '배우': 5701,\n",
              " '감독': 5538,\n",
              " '내용': 5283,\n",
              " '재미': 5250,\n",
              " '그냥': 5129,\n",
              " '좀': 4846,\n",
              " '쓰레기': 4629,\n",
              " '시간': 4577,\n",
              " '사랑': 4009,\n",
              " '작품': 3969,\n",
              " '볼': 3829,\n",
              " '하나': 3795,\n",
              " '마지막': 3652,\n",
              " '이건': 3591,\n",
              " '완전': 3356,\n",
              " '정도': 3348,\n",
              " '처음': 3264,\n",
              " '장면': 3241,\n",
              " '주인공': 3184,\n",
              " '액션': 3178,\n",
              " '최악': 3034,\n",
              " '이야기': 2919,\n",
              " '돈': 2903,\n",
              " '임': 2856,\n",
              " '별로': 2854,\n",
              " '연출': 2789,\n",
              " '느낌': 2773,\n",
              " '명작': 2695,\n",
              " '역시': 2651,\n",
              " '이해': 2553,\n",
              " '이영화': 2432,\n",
              " '안': 2404,\n",
              " '때문': 2355,\n",
              " '또': 2354,\n",
              " '여자': 2352,\n",
              " '꼭': 2195,\n",
              " '보기': 2160}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6w6mEQ_Aamt"
      },
      "source": [
        "## 단어 빈도 시각화"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEP8sDZuAbC9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5e43c319-d366-482a-a2e6-a860dca7ffd0"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "y_pos = np.arange(len(top_nouns))  # 0 ~ 49\n",
        "\n",
        "plt.figure(figsize = (10,20))\n",
        "plt.barh(y_pos, top_nouns.values())\n",
        "plt.yticks(y_pos, top_nouns.keys())\n",
        "plt.title(\"네이버 영화 리뷰 Top 50\")\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x2000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 843,
              "height": 1606
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOvekEYJNBHd"
      },
      "source": [
        "## 워드클라우드(WordCloud)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "094GGnh5N_jH"
      },
      "source": [
        "* 텍스트에 담겨있는 여러 형태소들의 등장 빈도를 가장 직관적으로 시각화하는 방법\n",
        "* 텍스트에 등장하는 단어를 그 등장 빈도에 따라 서로 크기가 다르게 구름 형태로 표현함으로써, 단어의 빈도 수를 한번에 알 수 있음  \n",
        "* 최근에 많은 서비스들이 어떤 핵심어가 많이 등장했는가를 워드클라우드 형식으로 시각화\n",
        "* 빈도 수만을 시각적으로 표현한 것이기 때문에, 단어들 사이의 연관성이나 의미 구조 등을 분석하는 데는 한계가 있음"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Py-tW4-whw5s"
      },
      "source": [
        "* 파이썬에서 워드 클라우드를 시각화하기 위해 `matplotlib` 라이브러리와 `WordCloud` 라이브러리를 `import` 해서 사용\n",
        "* `WordCloud` 라이브러리는 `pip install wordcloud` 명령어를 통해 설치 필요"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kARobmm2NDgl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11b1bbfd-39b7-4a82-ae95-b3bc9721bc8f"
      },
      "source": [
        "!pip install wordcloud"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.10/dist-packages (1.8.2.2)\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.10/dist-packages (from wordcloud) (1.22.4)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from wordcloud) (9.4.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from wordcloud) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (4.41.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (23.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (3.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHtxdWK5irIr"
      },
      "source": [
        "* WordCloud를 이용해 객체를 생성해주고,\n",
        "`generate_from_frequencies()` 함수로 빈도 수에 따라 워드클라우드 생성"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUYNxKiqOoDz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "565f642e-49f9-442c-84ef-a4c85f10e4ad"
      },
      "source": [
        "from wordcloud import WordCloud\n",
        "\n",
        "wc = WordCloud(\n",
        "    font_path = './font/NanumBarunGothic.ttf',\n",
        "    background_color='white'\n",
        ")\n",
        "\n",
        "wc.generate_from_frequencies(top_nouns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<wordcloud.wordcloud.WordCloud at 0x79bb7d1957e0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAND3HwGYscl"
      },
      "source": [
        "* 워드클라우드를 시각화할 때는 이미지 시각화 함수인 `imshow()` 함수를 사용해야 함"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c66M9M2ZYomO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "49c88911-4866-42b8-b526-a82ae7b382e8"
      },
      "source": [
        "figure = plt.figure(figsize = (12,12))\n",
        "ax = figure.add_subplot(1,1,1)\n",
        "ax.axis('off')\n",
        "ax.imshow(wc)\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
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          },
          "metadata": {
            "image/png": {
              "width": 950,
              "height": 484
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEY3ldWmNECt"
      },
      "source": [
        "## squarify 트리맵 시각화"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApiLK6KhZM3d"
      },
      "source": [
        "* `squarify`는 트리맵 생성을 지원해주는 파이썬 라이브러리\n",
        "* `squarify` 라이브러리를 이용해 키워드와 키워드 빈도 수를 트리맵으로 나타냄   "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RkTxL_qPqVnH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a660706d-1557-490f-d866-11e22b79f2ea"
      },
      "source": [
        "!pip install squarify"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: squarify in /usr/local/lib/python3.10/dist-packages (0.4.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cj_Y-VslNJDg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "5a7fbee2-f4c7-4c4e-aa16-f6f6c36613bd"
      },
      "source": [
        "import squarify\n",
        "\n",
        "norm = mpl.colors.Normalize(vmin=min(top_nouns.values()),\n",
        "                            vmax=max(top_nouns.values()))\n",
        "\n",
        "colors = [mpl.cm.Blues(norm(value)) for value in top_nouns.values()]\n",
        "\n",
        "squarify.plot(sizes=top_nouns.values(),\n",
        "              label=top_nouns.keys(),\n",
        "              color=colors,\n",
        "              alpha=0.7);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 563,
              "height": 417
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VHvZQv5xUV3z"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}