30. 네이버 영화 리뷰 데이터 분석 ② 데이터 시각화/네이버 영화 리뷰 데이터 분석 ② 데이터 시각화.ipynb

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  "metadata": {
    "colab": {
      "name": "네이버 영화 리뷰 데이터 분석 2. 데이터 시각화",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ndb796/Python-Data-Analysis-and-Image-Processing-Tutorial/blob/master/30.%20%EB%84%A4%EC%9D%B4%EB%B2%84%20%EC%98%81%ED%99%94%20%EB%A6%AC%EB%B7%B0%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EB%B6%84%EC%84%9D%20%E2%91%A1%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EC%8B%9C%EA%B0%81%ED%99%94/%EB%84%A4%EC%9D%B4%EB%B2%84%20%EC%98%81%ED%99%94%20%EB%A6%AC%EB%B7%B0%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EB%B6%84%EC%84%9D%20%E2%91%A1%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EC%8B%9C%EA%B0%81%ED%99%94.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5eccYF0jljXv",
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      },
      "source": [
        "## 네이버 영화 리뷰 데이터 분석 2. 데이터 시각화\n",
        "[강의 노트](https://github.com/ndb796/Python-Data-Analysis-and-Image-Processing-Tutorial/blob/master/30.%20%EB%84%A4%EC%9D%B4%EB%B2%84%20%EC%98%81%ED%99%94%20%EB%A6%AC%EB%B7%B0%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EB%B6%84%EC%84%9D%20%E2%91%A1%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EC%8B%9C%EA%B0%81%ED%99%94/%EB%84%A4%EC%9D%B4%EB%B2%84%20%EC%98%81%ED%99%94%20%EB%A6%AC%EB%B7%B0%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EB%B6%84%EC%84%9D%20%E2%91%A1%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EC%8B%9C%EA%B0%81%ED%99%94.pdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXQ6ehiZl9I3",
        "colab_type": "text"
      },
      "source": [
        "**리뷰 정보 클래스 작성하기**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23lTrMe9l9t3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import urllib.request\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "class Review:\n",
        "  def __init__(self, comment, date, star, good, bad):\n",
        "    self.comment = comment\n",
        "    self.date = date\n",
        "    self.star = star\n",
        "    self.good = good\n",
        "    self.bad = bad\n",
        "\n",
        "  def show(self):\n",
        "    print(\"내용: \" + self.comment +\n",
        "          \"\\n날짜: \" + self.date +\n",
        "          \"\\n별점: \" + self.star +\n",
        "          \"\\n좋아요: \" + self.good +\n",
        "          \"\\n싫어요: \" + self.bad)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyC0GtpKl_Z6",
        "colab_type": "text"
      },
      "source": [
        "**리뷰 정보 크롤링 함수**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBdSNYqnl_f4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def crawl(url):\n",
        "  soup = BeautifulSoup(urllib.request.urlopen(url).read(), \"html.parser\")\n",
        "  review_list = []\n",
        "  title = soup.find('h3', class_='h_movie').find('a').text\n",
        "  div = soup.find(\"div\", class_=\"score_result\")\n",
        "  data_list = div.select(\"ul > li\")\n",
        "  \n",
        "  for review in data_list:\n",
        "    star = review.find(\"div\", class_=\"star_score\").text.strip()\n",
        "    reply = review.find(\"div\", class_=\"score_reple\")\n",
        "    comment = reply.find(\"p\").text\n",
        "    date = reply.select(\"dt > em\")[1].text.strip()\n",
        "    button = review.find(\"div\", class_=\"btn_area\")\n",
        "    sympathy = button.select(\"strong > span\")\n",
        "    good = sympathy[0].text\n",
        "    bad = sympathy[1].text\n",
        "    review_list.append(Review(comment, date, star, good, bad))\n",
        "\n",
        "  return title, review_list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GiEnGholmFR",
        "colab_type": "text"
      },
      "source": [
        "**데이터 프레임화**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEFhfyCKljhP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "def get_summary(review_list):\n",
        "  star_list = []\n",
        "  good_list = []\n",
        "  bad_list = []\n",
        "  \n",
        "  for review in review_list:\n",
        "    star_list.append(int(review.star))\n",
        "    good_list.append(int(review.good))\n",
        "    bad_list.append(int(review.bad))\n",
        "    \n",
        "  star_series = pd.Series(star_list)\n",
        "  good_series = pd.Series(good_list)\n",
        "  bad_series = pd.Series(bad_list)\n",
        "  \n",
        "  summary = pd.DataFrame({\n",
        "      'Star': star_series,\n",
        "      'Good': good_series,\n",
        "      'Bad': bad_series,\n",
        "      'Score': good_series / (good_series + bad_series)\n",
        "  })\n",
        "  \n",
        "  return summary"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fXcSDgWmJxv",
        "colab_type": "text"
      },
      "source": [
        "**다수의 영화 데이터 시각화 ①**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oacbjGRlz2B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "outputId": "7cd7614a-25a1-4b94-cf62-252d75f08017"
      },
      "source": [
        "movie_code_list = [136900, 167657, 174321, 184859, 167391]\n",
        "review_lists = []\n",
        "\n",
        "for i in movie_code_list:\n",
        "  title, review_list = crawl(\"https://movie.naver.com/movie/bi/mi/basic.nhn?code=\" + str(i))\n",
        "  summary = get_summary(review_list)\n",
        "  print(\"[ %s ]\" % (title))\n",
        "  print(summary)\n",
        "  review_lists.append((title, review_list))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 어벤져스: 엔드게임 ]\n",
            "   Star   Good  Bad     Score\n",
            "0    10  22605  632  0.972802\n",
            "1    10  14885  420  0.972558\n",
            "2    10  14503  451  0.969841\n",
            "3    10  12470  518  0.960117\n",
            "4    10  11270  409  0.964980\n",
            "[ 명탐정 피카츄 ]\n",
            "   Star  Good  Bad     Score\n",
            "0    10  1873  102  0.948354\n",
            "1    10   946   81  0.921130\n",
            "2    10   800   70  0.919540\n",
            "3     8   924  282  0.766169\n",
            "4     8   581   34  0.944715\n",
            "[ 고양이 여행 리포트 ]\n",
            "   Star  Good  Bad     Score\n",
            "0    10    55    5  0.916667\n",
            "1    10    34    3  0.918919\n",
            "2     6    38   18  0.678571\n",
            "3    10    21    2  0.913043\n",
            "4    10    23    4  0.851852\n",
            "[ 물의 기억 ]\n",
            "   Star  Good  Bad     Score\n",
            "0    10    28   10  0.736842\n",
            "1    10    26   10  0.722222\n",
            "2    10    23    9  0.718750\n",
            "3    10    14    2  0.875000\n",
            "4    10    18    6  0.750000\n",
            "[ 더 캡틴 ]\n",
            "   Star  Good  Bad     Score\n",
            "0     9    65   10  0.866667\n",
            "1    10    35    4  0.897436\n",
            "2    10    34    7  0.829268\n",
            "3     8    28    5  0.848485\n",
            "4    10    25    5  0.833333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfdLRpDMmHNy",
        "colab_type": "text"
      },
      "source": [
        "**다수의 영화 데이터 시각화 ②**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjTLpd62pHTr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "0cf8b1d4-3650-4406-b3a5-86e2479031b9"
      },
      "source": [
        "!apt-get update -qq\n",
        "!apt-get install fonts-nanum* -qq"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 131183 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20170925-1_all.deb ...\n",
            "Unpacking fonts-nanum (20170925-1) ...\n",
            "Selecting previously unselected package fonts-nanum-eco.\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 (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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOacl1lyl53x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "outputId": "eafbb0fb-c09e-4249-95fa-ead513bc5d95"
      },
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager as fm\n",
        "\n",
        "font_path = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'\n",
        "fontprop = fm.FontProperties(fname=font_path, size=18)\n",
        "\n",
        "def movie_compare(review_lists):\n",
        "  count = 1\n",
        "  x = []\n",
        "  y = []\n",
        "  for movie, review_list in review_lists:\n",
        "    x.append(count)\n",
        "    summary = get_summary(review_list)\n",
        "    summary = summary[summary['Score'] > 0.8]\n",
        "    y.append(summary['Star'].mean())\n",
        "    count += 1\n",
        "  plt.bar(x, y)\n",
        "  plt.title('영화 별점 비교', fontproperties=fontprop)\n",
        "  plt.xlabel('영화 번호', fontproperties=fontprop)\n",
        "  plt.ylabel('신뢰성 별점 평균', fontproperties=fontprop)\n",
        "  plt.show()\n",
        "  \n",
        "movie_compare(review_lists)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d0kXABcAxkubZfrK2kaSDKDrGd2pTfR8EzipHSC0C/qVNx42IiBGqGhhTAGz/QdJbgd8C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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}