Analisis_Sentimen_Duolingo.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/LatiefDataVisionary/big-data-and-data-analytics-college-task/blob/main/Analisis_Sentimen_Duolingo.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",
      "source": [
        "**Judul Dataset** \t: Duolingo playstore appstore reviews dataset\n",
        "\n",
        "**Link Dataset**\t\t: https://www.kaggle.com/datasets/rajvardhanshendge/duolingo-playstore-appstore-reviews-dataset\n"
      ],
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      "execution_count": 1,
      "metadata": {
        "id": "1yXhgzv_6AW_"
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      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df = pd.read_csv('aplaystoreappstoreduolingoreviews.csv')"
      ],
      "metadata": {
        "id": "cSlLao3o6Ihh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnLxKSkm921Y",
        "outputId": "265f6f49-efae-45fb-8816-35a270f8347c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Big Data & Data Analytics/aplaystoreappstoreduolingoreviews.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9Pz8HeU97P_",
        "outputId": "0d5d5af4-f67b-4ac2-8c5b-d0c1313fae6b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-09db24209e67>:1: DtypeWarning: Columns (4,9,10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('/content/drive/MyDrive/Big Data & Data Analytics/aplaystoreappstoreduolingoreviews.csv')\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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      "execution_count": 15,
      "outputs": [
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              "   Unnamed: 0       source                             review_id  \\\n",
              "0           0  Google Play  acd36426-845f-499d-894e-75894944664d   \n",
              "1           1  Google Play  de1f3d24-328e-47d2-b8d2-176d3896e156   \n",
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              "3           3  Google Play  cb985479-b8ae-40b4-90e0-1e5fbf31a597   \n",
              "4           4  Google Play  747a16db-708d-4318-8001-eb0738580b67   \n",
              "\n",
              "           user_name review_title  \\\n",
              "0  Rajammal munusamy          NaN   \n",
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              "3       Ravi Prakash          NaN   \n",
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              "\n",
              "                                  review_description  rating  thumbs_up  \\\n",
              "0                            I love it and it's easy       5        0.0   \n",
              "1          This is the best application for learning       5        0.0   \n",
              "2  it's actually fun, I'm a beginner so I really ...       5        0.0   \n",
              "3  This is the best app to learn languages. It ma...       5        0.0   \n",
              "4                    Best app ever SPANISH OR VANISH       5        0.0   \n",
              "\n",
              "           review_date developer_response developer_response_date appVersion  \\\n",
              "0  2023-11-15 17:26:28                NaN                     NaN    5.125.4   \n",
              "1  2023-11-15 17:26:04                NaN                     NaN    5.128.3   \n",
              "2  2023-11-15 17:25:14                NaN                     NaN    5.128.3   \n",
              "3  2023-11-15 17:24:45                NaN                     NaN        NaN   \n",
              "4  2023-11-15 17:22:36                NaN                     NaN        NaN   \n",
              "\n",
              "  laguage_code country_code  \n",
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    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "CT3vBCIB7D4-",
        "outputId": "4d7340e8-f458-4db7-ad77-04867ffefad3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Unnamed: 0        rating     thumbs_up\n",
              "count  1.589521e+06  1.589521e+06  1.587521e+06\n",
              "mean   7.927625e+05  4.499267e+00  7.539403e-01\n",
              "std    4.588510e+05  1.050211e+00  1.759789e+01\n",
              "min    0.000000e+00  0.000000e+00  0.000000e+00\n",
              "25%    3.953800e+05  4.000000e+00  0.000000e+00\n",
              "50%    7.927600e+05  5.000000e+00  0.000000e+00\n",
              "75%    1.190140e+06  5.000000e+00  0.000000e+00\n",
              "max    1.587520e+06  5.000000e+00  6.742000e+03"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>rating</th>\n",
              "      <th>thumbs_up</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.589521e+06</td>\n",
              "      <td>1.589521e+06</td>\n",
              "      <td>1.587521e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.927625e+05</td>\n",
              "      <td>4.499267e+00</td>\n",
              "      <td>7.539403e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.588510e+05</td>\n",
              "      <td>1.050211e+00</td>\n",
              "      <td>1.759789e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.953800e+05</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.927600e+05</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.190140e+06</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.587520e+06</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>6.742000e+03</td>\n",
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              "        const element = document.querySelector('#df-e63241a6-8736-4151-a83f-77ed39f929d7');\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",
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              "    animation:\n",
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              "  }\n",
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              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 572618.7602098397,\n        \"min\": 0.0,\n        \"max\": 1589521.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          792762.5164813802,\n          792760.0,\n          1589521.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 561979.2990366579,\n        \"min\": 0.0,\n        \"max\": 1589521.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1589521.0,\n          4.499266760237832,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thumbs_up\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 560936.9396330914,\n        \"min\": 0.0,\n        \"max\": 1587521.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.753940262837468,\n          6742.0,\n          17.597893685133734\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "PIGqYkYR7U2d",
        "outputId": "b160d758-cf45-4b52-aa45-fbce75136c24"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0                       0\n",
              "source                           0\n",
              "review_id                        0\n",
              "user_name                        5\n",
              "review_title               1587521\n",
              "review_description            2210\n",
              "rating                           0\n",
              "thumbs_up                     2000\n",
              "review_date                      0\n",
              "developer_response         1589395\n",
              "developer_response_date    1589398\n",
              "appVersion                  177087\n",
              "laguage_code                     0\n",
              "country_code                     0\n",
              "dtype: int64"
            ],
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              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(df.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "V03K9f_k7g-V",
        "outputId": "5e203f0f-a795-450f-9710-bb29a2eb028e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mendapatkan jumlah baris dan kolom\n",
        "jumlah_baris, jumlah_kolom = df.shape\n",
        "\n",
        "# Menampilkan jumlah baris dan kolom\n",
        "print(f\"Jumlah baris dalam dataset: {jumlah_baris}\")\n",
        "print(f\"Jumlah baris dalam dataset: {jumlah_kolom}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B9CHiIyD7l_U",
        "outputId": "99f4cec5-7ebe-4fe6-fd34-9d32b594277b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah baris dalam dataset: 1589521\n",
            "Jumlah baris dalam dataset: 14\n"
          ]
        }
      ]
    }
  ]
}