NLP-final.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sungbinlee/NLP-Healthcare-App-Reviews/blob/main/NLP_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# [NLP] Mobile Apps Reviews Analysis using LDA Topic Modeling\n",
        "\n"
      ],
      "metadata": {
        "id": "OLpdY_L0LM1Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "by Sungbin Lee"
      ],
      "metadata": {
        "id": "WCS57fCOmYMB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Abstract\n"
      ],
      "metadata": {
        "id": "_bnfgupoLRxg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this project, I analyze customer needs through text mining of healthcare app reviews and based on this, I propose a design strategy for healthcare apps. I have collected 34,230 reviews from 10 healthcare apps in the Google Play Store. I performed LDA topic modeling to analyze customer needs in-depth through"
      ],
      "metadata": {
        "id": "F1zDKN0Ob-ww"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Dataset"
      ],
      "metadata": {
        "id": "vIc2lEYQL2JD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   2.1 User reviews\n",
        "\n",
        ">Collected 34,320 reviews from 10 health care apps in the Google Play Store\n",
        "\n",
        "> Data Collection Method: Crawiling the Google Play store\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "YavNp0rncPt-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 2.2 Dataset for preprocessing purposes\n",
        "> The dataset used for preprocessing purposes is as follows.\n",
        "\n",
        "\n",
        "1.   List for word substitution\n",
        "> Since LDA topic modeling provides results based on the most frequent vocabulary, unifying words with the same meaning into a single word is an effective way to perform semantic analysis on text. For example, 'iPhone' and 'galaxy s8' are both the same word as 'smartphone'. A human can determine that the words all have the same meaning, but the computer recognizes them as all different words. This may cause keywords to be missed because the number of occurrences of a word with a specific meaning is counted less as it is used as a different word even though it is a frequent vocabulary. Therefore, in text mining techniques where the frequency of occurrence of words is important, such as LDA topic modeling, prior word replacement is one of the ways to increase the effectiveness of data analysis.\n",
        "\n",
        "2.   List for stopword\n",
        "> A stopword is a word that appears frequently in text mining, but it is a word such as a predicate or investigation that is far from user's reactions or opinions. They have nothing to do with user experience (UX). Therefore, it is necessary to organize these stopwords well in the preprocessing stage."
      ],
      "metadata": {
        "id": "RBeHJrS8daGv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. LDA topic modeling concepts"
      ],
      "metadata": {
        "id": "gemKduS9L-aR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Topic Modeling is a text mining methodology that finds key topics in text-based document data. In particular, Latent Dirichlet Allocation (LDA) is the most representative algorithm for topic modeling. Specifically, LDA topic modeling analyzes which topics in a document and at what ratio by analyzing a large amount of document data through a probability-based modeling technique  [(Blei et al., 2003)](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf?ref=https://githubhelp.com). In addition, since it provides information on what keywords are configured for each topic, it has an effective advantage in deriving insights through keyword combinations. Recently, research has been actively conducted in various fields, such as automatically classifying similar topics on SNS through LDA topic modeling or deriving customer needs by analyzing airline online reviews ([Lu et al., 2013, ](https://ieeexplore.ieee.org/abstract/document/6454769)[Kwon et al., 2021](https://www.mdpi.com/2078-2489/12/2/78))"
      ],
      "metadata": {
        "id": "6eFQFU8QgUp9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. LDA topic modeling visualization"
      ],
      "metadata": {
        "id": "Zd9sfO1RMHVn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this project, considering the review rating is out of 5, I classified 4-5 as positive reviews and 1-2 reviews as negative reviews. LDA topic modeling was performed and visualized for each review group that received positive/negative ratings, as shown in Figures 1 and 2 below.![rel.PNG](data:image/png;base64,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        "\n",
        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      ],
      "metadata": {
        "id": "Z1opCRXvhJVC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Code"
      ],
      "metadata": {
        "id": "xkqY8gUpMTgZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.1 Google drive mount\n",
        "\n"
      ],
      "metadata": {
        "id": "ZADAlxW2_BwJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GWbRx3Yo9P2U",
        "outputId": "43094f7d-6f35-4e1d-aa84-1929d9db32df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.2 Package Install"
      ],
      "metadata": {
        "id": "AlQqVCe9MzJh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyLDAvis==2.1.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8vBXfAX8rCU",
        "outputId": "920ff194-86e8-4a29-b244-e564cf0b57c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pyLDAvis==2.1.2 in /usr/local/lib/python3.7/dist-packages (2.1.2)\n",
            "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (1.21.6)\n",
            "Requirement already satisfied: jinja2>=2.7.2 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (2.11.3)\n",
            "Requirement already satisfied: joblib>=0.8.4 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (1.2.0)\n",
            "Requirement already satisfied: pandas>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (1.3.5)\n",
            "Requirement already satisfied: wheel>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (0.37.1)\n",
            "Requirement already satisfied: numexpr in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (2.8.3)\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (3.6.4)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (0.16.0)\n",
            "Requirement already satisfied: funcy in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (1.17)\n",
            "Requirement already satisfied: scipy>=0.18.0 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis==2.1.2) (1.7.3)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.7.2->pyLDAvis==2.1.2) (2.0.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.17.0->pyLDAvis==2.1.2) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.17.0->pyLDAvis==2.1.2) (2022.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.17.0->pyLDAvis==2.1.2) (1.15.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from numexpr->pyLDAvis==2.1.2) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->numexpr->pyLDAvis==2.1.2) (3.0.9)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from pytest->pyLDAvis==2.1.2) (22.1.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from pytest->pyLDAvis==2.1.2) (57.4.0)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest->pyLDAvis==2.1.2) (8.14.0)\n",
            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.7/dist-packages (from pytest->pyLDAvis==2.1.2) (0.7.1)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->pyLDAvis==2.1.2) (1.11.0)\n",
            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.7/dist-packages (from pytest->pyLDAvis==2.1.2) (1.4.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.3 Package import"
      ],
      "metadata": {
        "id": "U80zlCrN_My4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import warnings # ignore warning msg\n",
        "warnings.filterwarnings(action='ignore')\n",
        "# Use NLTK\n",
        "import nltk\n",
        "import pickle\n",
        "import re\n",
        "nltk.download('all')\n",
        "\n",
        "from tqdm import tqdm # work process visualization\n",
        "import re # Regular expression package for string\n",
        "from gensim import corpora # word frequency counting package\n",
        "import gensim #LDA\n",
        "import pyLDAvis\n",
        "import pyLDAvis.gensim\n",
        "from collections import Counter"
      ],
      "metadata": {
        "id": "2m7Z_5rD7nTV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.4 Dataset load"
      ],
      "metadata": {
        "id": "F3NWx5hm_Rwc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_raw = pd.read_excel('/content/gdrive/MyDrive/NLP-final-project/dataset_raw.xlsx')\n",
        "dataset_raw.head()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tehHu8jn9sz8",
        "outputId": "05aed22a-9873-4361-966e-88f51622d1c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              app  \\\n",
              "0  FitCoach: Fitness Coach & Diet   \n",
              "1  FitCoach: Fitness Coach & Diet   \n",
              "2  FitCoach: Fitness Coach & Diet   \n",
              "3  FitCoach: Fitness Coach & Diet   \n",
              "4  FitCoach: Fitness Coach & Diet   \n",
              "\n",
              "                                              review  rating  \n",
              "0  Its a nice app but so many features don't actu...       3  \n",
              "1  Deceptive. Not at all as advertised. Annoying ...       1  \n",
              "2  Not the app shown in the Facebook ads. I signe...       1  \n",
              "3  Updated review. The app is good, the range of ...       4  \n",
              "4  Was alright in the beginning. You can't change...       2  "
            ],
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              "\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.5 Data exploration"
      ],
      "metadata": {
        "id": "eJ-SDtGc_W_k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_raw.info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLto5jSe_V79",
        "outputId": "e1f17f7a-be91-4a63-ad9b-a2588ba7d5d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of                                   app  \\\n",
              "0      FitCoach: Fitness Coach & Diet   \n",
              "1      FitCoach: Fitness Coach & Diet   \n",
              "2      FitCoach: Fitness Coach & Diet   \n",
              "3      FitCoach: Fitness Coach & Diet   \n",
              "4      FitCoach: Fitness Coach & Diet   \n",
              "...                               ...   \n",
              "34315    8fit Workouts & Meal Planner   \n",
              "34316    8fit Workouts & Meal Planner   \n",
              "34317    8fit Workouts & Meal Planner   \n",
              "34318    8fit Workouts & Meal Planner   \n",
              "34319    8fit Workouts & Meal Planner   \n",
              "\n",
              "                                                  review  rating  \n",
              "0      Its a nice app but so many features don't actu...       3  \n",
              "1      Deceptive. Not at all as advertised. Annoying ...       1  \n",
              "2      Not the app shown in the Facebook ads. I signe...       1  \n",
              "3      Updated review. The app is good, the range of ...       4  \n",
              "4      Was alright in the beginning. You can't change...       2  \n",
              "...                                                  ...     ...  \n",
              "34315        It's the perfect app for a perfect workout.       5  \n",
              "34316      good app. i like the meal plans and workouts.       5  \n",
              "34317  Easy and practical to use. Love the variety of...       5  \n",
              "34318                            It helps ME and MY body       5  \n",
              "34319           Good app so far with the first exercise.       5  \n",
              "\n",
              "[34320 rows x 3 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.6 Data pre-processing"
      ],
      "metadata": {
        "id": "U1VsOL25_k8o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 1) Check for missing values"
      ],
      "metadata": {
        "id": "XbPNIery_vEV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_raw.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KIuvf4L_n86",
        "outputId": "ccc39f99-39f6-472b-95ed-7b57e35b5365"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "app       0\n",
              "review    2\n",
              "rating    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 2) Remove missing values"
      ],
      "metadata": {
        "id": "7-tP9Olm_-_I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# axis = 0: remove missing value's row\n",
        "dataset = dataset_raw.dropna(axis = 0)\n",
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbhrFasT_2Z1",
        "outputId": "5a01140f-a00c-4a51-ee0b-9ecb879a08ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "app       0\n",
              "review    0\n",
              "rating    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 3) Load Dictionary for Preprocessing"
      ],
      "metadata": {
        "id": "tOaN_44zAKzn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stopword_list = pd.read_excel('/content/gdrive/MyDrive/NLP-final-project/stopword_list.xlsx')\n",
        "stopword_list.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "iqnd9bxDAvdZ",
        "outputId": "a2dcb177-6601-4943-8383-6b76d26539b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    stopword\n",
              "147   really\n",
              "148    great\n",
              "149     nice\n",
              "150     like\n",
              "151     love"
            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "replace_list = pd.read_excel('/content/gdrive/MyDrive/NLP-final-project/replace_list.xlsx')\n",
        "replace_list.head()"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  before_replacement after_replacement\n",
              "0         cell phone             phone\n",
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              "2             iphone             phone\n",
              "3             galaxy             phone\n",
              "4               ipad             phone"
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-c7638691-0cae-4b9c-8236-471b5ba190f4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c7638691-0cae-4b9c-8236-471b5ba190f4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 4) Word substitution"
      ],
      "metadata": {
        "id": "2G4jmSHNBAaO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_word(review):\n",
        "    for i in range(len(replace_list['before_replacement'])):\n",
        "        try:\n",
        "            # Perform data replacement only when there is a word to be replaced\n",
        "            if replace_list['before_replacement'][i] in review:\n",
        "                review = review.replace(replace_list['before_replacement'][i], replace_list['after_replacement'][i])\n",
        "        except Exception as e:\n",
        "            print(f\"Error: {e}\")\n",
        "    return review"
      ],
      "metadata": {
        "id": "vckfNHSaBRbp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['review_prep'] = ''\n",
        "review_replaced_list = []\n",
        "for review in tqdm(dataset['review']):\n",
        "    review_replaced = replace_word(str(review)).lower() #lower case\n",
        "    review_replaced_list.append(review_replaced)\n",
        "dataset['review_prep'] = review_replaced_list\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "PbFOuDPnBkcf",
        "outputId": "b9f6cad0-97bc-44b2-d140-8b3cfa0e78a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 34318/34318 [00:05<00:00, 6800.80it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              app  \\\n",
              "0  FitCoach: Fitness Coach & Diet   \n",
              "1  FitCoach: Fitness Coach & Diet   \n",
              "2  FitCoach: Fitness Coach & Diet   \n",
              "3  FitCoach: Fitness Coach & Diet   \n",
              "4  FitCoach: Fitness Coach & Diet   \n",
              "\n",
              "                                              review  rating  \\\n",
              "0  Its a nice app but so many features don't actu...       3   \n",
              "1  Deceptive. Not at all as advertised. Annoying ...       1   \n",
              "2  Not the app shown in the Facebook ads. I signe...       1   \n",
              "3  Updated review. The app is good, the range of ...       4   \n",
              "4  Was alright in the beginning. You can't change...       2   \n",
              "\n",
              "                                         review_prep  \n",
              "0  its a nice application but so many features do...  \n",
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              "2  not the application shown in the facebook ads....  \n",
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              "      <td>FitCoach: Fitness Coach &amp; Diet</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 5) Remove non-English text"
      ],
      "metadata": {
        "id": "BCtqE0_3CRc4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "review_removed = list(map(lambda review: re.sub('[^a-zA-Z ]', '', review), dataset['review_prep']))\n",
        "dataset['review_prep'] = review_removed"
      ],
      "metadata": {
        "id": "YgeXAb6cCQ_2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 6) Separation of data based on rating\n",
        "\n",
        "The Google Play Store has a rating of 5 out of 5. Therefore, in this project, 4-5 were classified as positive reviews and 1-2  were classified as negative reviews. This is to distinguish between positive and negative reviews related to the experience of using the service."
      ],
      "metadata": {
        "id": "HroBlOfODAU-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Positive review (4, 5 out of 5)\n",
        "review_pos = dataset[(4 == dataset['rating']) | (dataset['rating'] == 5)]['review_prep']\n",
        "# Negative review (1, 2 out of 5)\n",
        "review_neg = dataset[(1 == dataset['rating']) | (dataset['rating'] == 2)]['review_prep']"
      ],
      "metadata": {
        "id": "eRLaAyn4DB6b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_pos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZ2ountkHIfB",
        "outputId": "46fe7ddb-c8b2-4325-dcdd-73a7ce28ee55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3        updated review the application is good the ran...\n",
              "13       great way to stay on track with lots of variet...\n",
              "18       despite the negative reviews i read i find the...\n",
              "20       update after posting review my next workout di...\n",
              "21       i loved this application while using it it kic...\n",
              "                               ...                        \n",
              "34315    its the perfect application for a perfect workout\n",
              "34316    good application i like the meal plans and wor...\n",
              "34317    easy and practical to use love the variety of ...\n",
              "34318                              it helps me and my body\n",
              "34319      good application so far with the first exercise\n",
              "Name: review_prep, Length: 21017, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 7) Tokenization\n",
        "\n",
        "Nouns are a key morpheme to understand the context in a sentence and have the advantage of being able to easily identify frequent words, only nouns are extracted from the review."
      ],
      "metadata": {
        "id": "7uHT-7WqDq_y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "review_tokenized_pos = list(map(lambda review: nltk.word_tokenize(review), review_pos))\n",
        "review_tokenized_neg = list(map(lambda review: nltk.word_tokenize(review), review_neg))"
      ],
      "metadata": {
        "id": "FQdSKjeuDqqT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 8) Remove stopwords"
      ],
      "metadata": {
        "id": "c4a3jbKnE0_L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_stopword(tokens):\n",
        "    review_removed_stopword = []\n",
        "    for token in tokens:\n",
        "        # When the number of characters in the token is 2 or more\n",
        "        if 1 < len(token):\n",
        "            # Include as review data for analytics only if the token is not a stopword\n",
        "            if token not in list(stopword_list['stopword']):\n",
        "                review_removed_stopword.append(token)\n",
        "    return review_removed_stopword"
      ],
      "metadata": {
        "id": "jP6H1z_jEy8N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_removed_stopword_pos = list(map(lambda tokens : remove_stopword(tokens), review_tokenized_pos))\n",
        "review_removed_stopword_neg = list(map(lambda tokens : remove_stopword(tokens), review_tokenized_neg))"
      ],
      "metadata": {
        "id": "-QxmqxxkFOLk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 9) Select a specific range of reviews\n",
        "\n",
        "In general, the longer the review, the more likely it will contain user feedback, such as user experience or technical issues. However, reviews that are rather long may have difficulties in identifying topics or extracting features using combinations of words in the review [(Vasa et al., 2012)](https://dl.acm.org/doi/abs/10.1145/2414536.2414577?casa_token=OQqZbuik4RwAAAAA:_FOT4Fl0bj1reHDV7BNObsGIM1XR6Z7o3TZyBQeIoIX6aEGcUMKhUSMOQv0ytkc9lu3_x7ZI6jDbTVc). Therefore, in this project, only reviews with 3 or more and 15 or less nouns extracted from each review were used for analysis.\n"
      ],
      "metadata": {
        "id": "xyYTkNXeFUXE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MIN_TOKEN_NUMBER = 3 # Min\n",
        "MAX_TOKEN_NUMBER = 15 # Max"
      ],
      "metadata": {
        "id": "DAVp2DTDFUHb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def select_review(review_removed_stopword):\n",
        "    review_prep = []\n",
        "    for tokens in review_removed_stopword:\n",
        "        if MIN_TOKEN_NUMBER <= len(tokens) <= MAX_TOKEN_NUMBER:\n",
        "            review_prep.append(tokens)\n",
        "    return review_prep"
      ],
      "metadata": {
        "id": "O7HO5i3SF23f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_prep_pos = select_review(review_removed_stopword_pos)\n",
        "review_prep_neg = select_review(review_removed_stopword_neg)"
      ],
      "metadata": {
        "id": "6hvqbm9eF4d1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 10) Check the preprocessing result"
      ],
      "metadata": {
        "id": "G2wfyw56H-UN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "review_num_pos = len(review_prep_pos)\n",
        "review_num_neg = len(review_prep_neg)\n",
        "review_num_tot = review_num_pos + review_num_neg\n",
        "\n",
        "print(f\"Total: {review_num_tot}\")\n",
        "print(f\"Positive Reviews: {review_num_pos}({(review_num_pos/review_num_tot)*100:.2f}%)\")\n",
        "print(f\"Negative Reviews: {review_num_neg}({(review_num_neg/review_num_tot)*100:.2f}%)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80kimx0uF6jp",
        "outputId": "7ffe771d-2013-46b9-ab62-135c8e150cb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total: 13167\n",
            "Positive Reviews: 10306(78.27%)\n",
            "Negative Reviews: 2861(21.73%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.7 LDA Topic Modeling\n",
        "\n",
        "> 1) Hyperparameter tuning"
      ],
      "metadata": {
        "id": "PZFNwBpxISlF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NUM_TOPICS = 10\n",
        "# passes:  The same concept as the epoch, determining the number of times to train the model with the entire corpus\n",
        "PASSES = 15"
      ],
      "metadata": {
        "id": "HxQdj2-KIIPH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 2) Model training"
      ],
      "metadata": {
        "id": "cn66AE34IsXf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lda_modeling(review_prep):\n",
        "    # Word encoding and frequency counting\n",
        "    dictionary = corpora.Dictionary(review_prep)\n",
        "    corpus = [dictionary.doc2bow(review) for review in review_prep]\n",
        "    # LDA model training\n",
        "    model = gensim.models.ldamodel.LdaModel(corpus,\n",
        "                                            num_topics = NUM_TOPICS,\n",
        "                                            id2word = dictionary,\n",
        "                                            passes = PASSES)\n",
        "    return model, corpus, dictionary"
      ],
      "metadata": {
        "id": "EIreGJmTIvWk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 3) Word composition output function by topic"
      ],
      "metadata": {
        "id": "kud263kFI6HW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def print_topic_prop(topics, RATING):\n",
        "    topic_values = []\n",
        "    for topic in topics:\n",
        "        topic_value = topic[1]\n",
        "        topic_values.append(topic_value)\n",
        "    topic_prop = pd.DataFrame({\"topic_num\" : list(range(1, NUM_TOPICS + 1)), \"word_prop\": topic_values})\n",
        "    topic_prop.to_excel('/content/gdrive/MyDrive/NLP-final-project/result/topic_prop_' + RATING +  '.xlsx')\n",
        "    display(topic_prop)"
      ],
      "metadata": {
        "id": "FJrVdxABI5hm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 4) Visualization function"
      ],
      "metadata": {
        "id": "DP39XQI_JVel"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lda_visualize(model, corpus, dictionary, RATING):\n",
        "    pyLDAvis.enable_notebook()\n",
        "    result_visualized = pyLDAvis.gensim.prepare(model, corpus, dictionary)\n",
        "    pyLDAvis.display(result_visualized)\n",
        "    # Save result\n",
        "    RESULT_FILE = '/content/gdrive/MyDrive/NLP-final-project/result/lda_result_' + RATING + '.html'\n",
        "    pyLDAvis.save_html(result_visualized, RESULT_FILE)"
      ],
      "metadata": {
        "id": "M0SMD262JaDU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 5) Modeling positive review topics\n",
        "\n",
        "\n",
        "Using the previously defined model training, topic-specific word composition output function, and visualization function, I will train and visualize the topic modeling model for each positive review and negative review. Here, a total of 10 constituent words (=NUM_WORDS) per topic were set."
      ],
      "metadata": {
        "id": "ksTvRVWqJiVL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model, corpus, dictionary = lda_modeling(review_prep_pos)\n",
        "NUM_WORDS = 10"
      ],
      "metadata": {
        "id": "Ckl4kgnXJtQJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RATING = 'pos'\n",
        "topics = model.print_topics(num_words = NUM_WORDS)\n",
        "print_topic_prop(topics, RATING)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "PVo7vPkwJw0V",
        "outputId": "2201eee9-5794-47cf-f92b-0db797d26eef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   topic_num                                          word_prop\n",
              "0          1  0.094*\"workout\" + 0.066*\"best\" + 0.026*\"fitnes...\n",
              "1          2  0.122*\"use\" + 0.114*\"easy\" + 0.028*\"simple\" + ...\n",
              "2          3  0.044*\"weight\" + 0.039*\"add\" + 0.020*\"training...\n",
              "3          4  0.056*\"easy\" + 0.046*\"workouts\" + 0.024*\"amazi...\n",
              "4          5  0.045*\"workouts\" + 0.028*\"meal\" + 0.025*\"time\"...\n",
              "5          6  0.045*\"workouts\" + 0.043*\"free\" + 0.029*\"worko...\n",
              "6          7  0.022*\"workout\" + 0.022*\"exercises\" + 0.020*\"g...\n",
              "7          8  0.019*\"get\" + 0.013*\"recipes\" + 0.013*\"music\" ...\n",
              "8          9  0.021*\"fit\" + 0.020*\"update\" + 0.016*\"wish\" + ...\n",
              "9         10  0.051*\"track\" + 0.038*\"keep\" + 0.023*\"keeps\" +..."
            ],
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              "      <th></th>\n",
              "      <th>topic_num</th>\n",
              "      <th>word_prop</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.094*\"workout\" + 0.066*\"best\" + 0.026*\"fitnes...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.122*\"use\" + 0.114*\"easy\" + 0.028*\"simple\" + ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.044*\"weight\" + 0.039*\"add\" + 0.020*\"training...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.056*\"easy\" + 0.046*\"workouts\" + 0.024*\"amazi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.045*\"workouts\" + 0.028*\"meal\" + 0.025*\"time\"...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.045*\"workouts\" + 0.043*\"free\" + 0.029*\"worko...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.022*\"workout\" + 0.022*\"exercises\" + 0.020*\"g...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.019*\"get\" + 0.013*\"recipes\" + 0.013*\"music\" ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.021*\"fit\" + 0.020*\"update\" + 0.016*\"wish\" + ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.051*\"track\" + 0.038*\"keep\" + 0.023*\"keeps\" +...</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
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          "metadata": {}
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    {
      "cell_type": "code",
      "source": [
        "lda_visualize(model, corpus, dictionary, RATING)"
      ],
      "metadata": {
        "id": "O-T0ANJ-KWAO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 6) Modeling Negative review topics"
      ],
      "metadata": {
        "id": "bUBnxJEXLSNQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model, corpus, dictionary = lda_modeling(review_prep_neg)\n",
        "NUM_WORDS = 10"
      ],
      "metadata": {
        "id": "h15vD7IqLZYb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RATING = 'neg'\n",
        "topics = model.print_topics(num_words = NUM_WORDS)\n",
        "print_topic_prop(topics, RATING)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "6bmE0wakLaLB",
        "outputId": "db37293e-8d77-4a29-a4fe-75845951df27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   topic_num                                          word_prop\n",
              "0          1  0.020*\"year\" + 0.013*\"lost\" + 0.011*\"app\" + 0....\n",
              "1          2  0.026*\"sign\" + 0.026*\"google\" + 0.015*\"keeps\" ...\n",
              "2          3  0.028*\"charged\" + 0.027*\"subscription\" + 0.022...\n",
              "3          4  0.020*\"steps\" + 0.016*\"working\" + 0.016*\"data\"...\n",
              "4          5  0.036*\"steps\" + 0.015*\"track\" + 0.014*\"count\" ...\n",
              "5          6  0.023*\"use\" + 0.017*\"account\" + 0.016*\"dont\" +...\n",
              "6          7  0.039*\"pay\" + 0.022*\"free\" + 0.019*\"money\" + 0...\n",
              "7          8  0.018*\"keeps\" + 0.013*\"plan\" + 0.013*\"used\" + ...\n",
              "8          9  0.024*\"update\" + 0.019*\"use\" + 0.018*\"time\" + ...\n",
              "9         10  0.035*\"free\" + 0.026*\"download\" + 0.022*\"get\" ..."
            ],
            "text/html": [
              "\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>topic_num</th>\n",
              "      <th>word_prop</th>\n",
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              "      <td>0.026*\"sign\" + 0.026*\"google\" + 0.015*\"keeps\" ...</td>\n",
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              "      <th>2</th>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.036*\"steps\" + 0.015*\"track\" + 0.014*\"count\" ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.023*\"use\" + 0.017*\"account\" + 0.016*\"dont\" +...</td>\n",
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              "      <th>7</th>\n",
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              "      <th>9</th>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5943bad4-fdb9-4a30-8e06-9345b10d23ae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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    {
      "cell_type": "code",
      "source": [
        "lda_visualize(model, corpus, dictionary, RATING)"
      ],
      "metadata": {
        "id": "nUCG1R7VLbHD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. How to interpret the results\n",
        "\n",
        "LDA topic modeling provides information on which keywords are composed of each topic and in what ratio. In other words, the user should understand the specific content of the topic through keywords. For example, a topic consisting of keywords such as 'workouts', 'progress', 'easy', and 'plans' will most likely be related to the 'exercise record' feature. As such, it is important for the LDA topic modeling technique to identify which keywords are in the topic and in what ratio. Considering these characteristics, I will discuss how to effectively interpret the data visualized through pyLDAvis."
      ],
      "metadata": {
        "id": "1ylW0-weLcQr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 6.1 Relevance\n",
        "\n",
        "Relevance() can be adjusted through the sliding bar on the upper right in the figure below. Relevance is a hyperparameter that balances the frequency of occurrence of a word in a topic with the frequency of its occurrence in the entire document. That is, when there is a word with a high frequency of appearance in a specific topic, whether the word has a high frequency of appearance because it is a keyword that distinguishes the topic from other topics, or simply because it is a word widely used in various document data. It is a parameter that helps to clearly distinguish whether or not was high.\n",
        "![rel.PNG](data:image/png;base64,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        "Figure 1. Review topic modeling visualization results of positive evaluation\n",
        "\n",
        "The Relevance value is a value between 0 and 1, and the closer it is to 0, the less the number of occurrences in the entire document is, but the focus is on whether the topic is a word that can differentiate it from other topics. On the other hand, the closer the Relevance value is to 1, the more likely it is to be a keyword that appears frequently in the entire document data rather than a keyword constituting a specific topic. For example, in healthcare app review data, the word 'exercise' is very likely to appear in multiple reviews. Therefore, it is difficult to clearly distinguish one topic from another by simply using the word 'exercise'. In these cases, setting the Relevance close to zero can penalize the importance of the word 'exercise', which appears in many reviews. This shows that the word 'exercise' is a widely used word throughout the document, rather than only appearing a lot in that topic. According to a study by [Sievert & Shirley (2014)](https://aclanthology.org/W14-3110.pdf), a Relevance value of 0.6 is known to be the most effective. However, this value is not always correct. This is because the optimal Relevance value may differ depending on each research domain, dataset, etc."
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "> 6.2 Topics and keywords\n",
        "\n",
        "All circles on the left in the figure below are each topic. The distance between circles means how similar topics are to each other. A larger circle means that the topic has more words (=tokens). If you hover your mouse over the circle, the ratio of the words constituting the topic to the total document data is displayed on the right side of the current topic's keywords. It also provides the ratio of the words that the topic constitutes to the words of the entire document data. In this way, by identifying which words are composed of each topic and at what ratio, the topic of each topic can be inferred, and furthermore, which topic is composed of the entire document data and at what ratio (= importance).\n",
        "\n",
        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)\n",
        "\n",
        "Figure 1. Review topic modeling visualization results of Negative evaluation"
      ],
      "metadata": {
        "id": "7p8Nj9bWXwCa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "7. insights\n",
        "\n",
        "Analyze user needs based on the visualization results."
      ],
      "metadata": {
        "id": "39j3BL_gZHpf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 7.1 Positive review analysis\n",
        "\n",
        "First, the results of topic modeling of positive reviews are as follows.\n",
        "\n",
        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)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "-P-EzzQPO4O1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nKwmD_Laii7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1) Exercise action description function\n",
        "\n",
        "You can see that the words 'useful', 'accurate', 'easy', 'simple', 'steps', and 'follow' appear frequently in topic 2. It can be interpreted that content that explains the movement of exercise step by step received positive reviews. Therefore, when planning a health care app service, I can consider video-based exercise action lecture content."
      ],
      "metadata": {
        "id": "IIVzW4awfZU6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2) Meal record feature\n",
        "\n",
        "In topic 5, words such as 'meal', 'track', 'schedule', and 'plans' appeared frequently. Through this, it can be interpreted that the meal record function, such as taking a picture of the meal and saving it, had a positive effect on controlling the diet. Computer Vision technology allows you to analyze what food you ate and how much you ate through food photos. This technology is expected to provide a positive user experience in terms of convenience by reducing the hassle of having to record dietary information one by one."
      ],
      "metadata": {
        "id": "7xQsxvCIhR_u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3) exercise record feature\n",
        "\n",
        "In topic 10, words such as 'workouts', 'progress', 'motivated', 'steps', 'track', and 'helps' appeared frequently. This can be interpreted as having a positive effect on providing interest and motivation for exercise by making an exercise plan through exercise log recording and checking exercise quantity through exercise amount measurement. As such, adding an exercise log recording feature to the health caring app service that can help users record the amount of exercise and type of exercises, is expected to help promote regular exercise and use of the app."
      ],
      "metadata": {
        "id": "AGM76w9vaDkc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 7.2 Negative review analysis\n",
        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      ],
      "metadata": {
        "id": "2YjJOpApill_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1) Automatic paid subscription complaint\n",
        "\n",
        "You can see that words such as 'charged', 'refund', 'subscription', 'trial', 'cancel', and 'free' appear frequently in topic 3. This is a number of complaints caused by the operating method of some healthcare apps provide free services for the first 1 to 3 months and then switch to paid subscription services without the user's additional consent after the trial period.  There are a lot of reviews in these service policies, where many users request immediate cancellation of subscription and refund. Therefore, in the healthcare app operation method, it is necessary to switch to providing payment and paid service only when the user is additionally asked for payment before switching to a paid subscription service and agrees to this."
      ],
      "metadata": {
        "id": "HSGJR9T5ivyF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2) Exercise tracking accuracy issue\n",
        "\n",
        "You can see that the words 'steps', 'track', 'count', 'accurate', 'gps', 'distance', 'mile', 'work', and 'wearable' appear frequently in topic 5. There is a lot of negative feedback related to accuracy issues in exercise tracking, such as step count. For example, the app says that you have taken 10,000 steps, but your smartwatch only counts 5,000 steps. Users may underestimate the reliability of the service as a whole because of the low accuracy of these workout tracking. Therefore, when designing a healthcare service, it is necessary to continuously improve the accuracy of exercise tracking not only in the app but also in the wearable device environment."
      ],
      "metadata": {
        "id": "FpkPG28_iwFT"
      }
    }
  ]
}