NLPmodel.ipynb

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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/RiturajSaha/App-Review-Analyzer/blob/main/NLPmodel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "m7TgoYyqUbwt",
        "outputId": "897bf152-1f00-4d07-d9c1-83f5886a4d4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks/Review Analysis\n"
          ]
        }
      ],
      "source": [
        "cd '/content/drive/MyDrive/Colab Notebooks/Review Analysis'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing the Libraries"
      ],
      "metadata": {
        "id": "Rf09XxkpkvHm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "G1naraONO6-S",
        "outputId": "f0879d43-dc10-4318-ffe9-65107b97b903"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "import pickle\n",
        "import nltk\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining required Functions"
      ],
      "metadata": {
        "id": "lOR25BA7kzeW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CiU86jHcaHMX"
      },
      "outputs": [],
      "source": [
        "def classifyCol(df,col):\n",
        "  from sklearn.preprocessing import LabelEncoder\n",
        "  le = LabelEncoder()\n",
        "  df[col] = le.fit_transform(df[col])\n",
        "  print('For',col,'column:')\n",
        "  print(dict(zip(le.classes_, le.transform(le.classes_))))\n",
        "  return df\n",
        "\n",
        "\n",
        "def cleanCol(df,col):\n",
        "  reviews=[]\n",
        "  for row in df[col]:\n",
        "     row=re.sub('[^a-z]', ' ', row.lower())\n",
        "     stopWords = nltk.corpus.stopwords.words('english')\n",
        "     stopWords.remove('not')\n",
        "     row=row.split()\n",
        "     row = [nltk.stem.porter.PorterStemmer().stem(word) for word in row if not word in set(stopWords)]\n",
        "     row = ' '.join(row)\n",
        "     reviews.append(row)\n",
        "  df[col]=reviews\n",
        "  return df\n",
        "\n",
        "def getAcc(model, x_train, x_test, y_train, y_test) : \n",
        "  from sklearn.metrics import r2_score, mean_absolute_error, accuracy_score, confusion_matrix, classification_report \n",
        "  y_pred = model.predict(x_test)\n",
        "  print(\"Accuracy:\",accuracy_score(y_test, y_pred)*100) \n",
        "  print(\"R2 Score:\",r2_score(y_test, y_pred)) \n",
        "  print(\"Mean Absolute Error:\",mean_absolute_error(y_test, y_pred))\n",
        "  print(\"Confusion Matrix:\\n\",confusion_matrix(y_test, y_pred))\n",
        "  print(\"Classification Report:\\n\",classification_report(y_test, y_pred)) \n",
        "\n",
        "def bagOfWords(x):\n",
        "  from sklearn.feature_extraction.text import CountVectorizer\n",
        "  cv = CountVectorizer()\n",
        "  vector = cv.fit_transform(x).toarray()\n",
        "  mf=len(vector[0])\n",
        "  mf=int(str(mf)[:-2]+'00')\n",
        "  cv = CountVectorizer(max_features=mf)\n",
        "  x = cv.fit_transform(x).toarray()\n",
        "  return (x)\n",
        "   "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing the Dataset"
      ],
      "metadata": {
        "id": "X0cHrAbMkybe"
      }
    },
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              "      <th></th>\n",
              "      <th>App</th>\n",
              "      <th>Translated_Review</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>I like eat delicious food. That's I'm cooking ...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.533333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>This help eating healthy exercise regular basis</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.288462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10 Best Foods for You</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>Works great especially going grocery store</td>\n",
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              "      <td>Best idea us</td>\n",
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              "      <td>Houzz Interior Design Ideas</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>Houzz Interior Design Ideas</td>\n",
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              "      <td>Houzz Interior Design Ideas</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
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              "<p>64295 rows × 5 columns</p>\n",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "                               App  ... Sentiment_Subjectivity\n",
              "0            10 Best Foods for You  ...               0.533333\n",
              "1            10 Best Foods for You  ...               0.288462\n",
              "2            10 Best Foods for You  ...                    NaN\n",
              "3            10 Best Foods for You  ...               0.875000\n",
              "4            10 Best Foods for You  ...               0.300000\n",
              "...                            ...  ...                    ...\n",
              "64290  Houzz Interior Design Ideas  ...                    NaN\n",
              "64291  Houzz Interior Design Ideas  ...                    NaN\n",
              "64292  Houzz Interior Design Ideas  ...                    NaN\n",
              "64293  Houzz Interior Design Ideas  ...                    NaN\n",
              "64294  Houzz Interior Design Ideas  ...                    NaN\n",
              "\n",
              "[64295 rows x 5 columns]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "df=pd.read_csv('googleplaystore_user_reviews.csv')\n",
        "display(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cleaning and Processing the Dataframe"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For SENTIMENT column:\n",
            "{'Negative': 0, 'Neutral': 1, 'Positive': 2}\n"
          ]
        },
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>REVIEW</th>\n",
              "      <th>SENTIMENT</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>like eat delici food cook food case best food ...</td>\n",
              "      <td>2</td>\n",
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              "      <th>1</th>\n",
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              "      <td>2</td>\n",
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              "      <th>3</th>\n",
              "      <td>work great especi go groceri store</td>\n",
              "      <td>2</td>\n",
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              "      <th>4</th>\n",
              "      <td>best idea us</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>best way</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>64222</th>\n",
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              "      <td>properti busi got link sm happi perform still ...</td>\n",
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              "      <th>64230</th>\n",
              "      <td>useless app search flat kondapur hyderabad non...</td>\n",
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              "<p>37427 rows × 2 columns</p>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3a6494c6-4ef1-45e2-ad9f-80ed4b1fa3f6');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                  REVIEW  SENTIMENT\n",
              "0      like eat delici food cook food case best food ...          2\n",
              "1                  help eat healthi exercis regular basi          2\n",
              "3                     work great especi go groceri store          2\n",
              "4                                           best idea us          2\n",
              "5                                               best way          2\n",
              "...                                                  ...        ...\n",
              "64222  ad older mani agent not much owner post detail...          2\n",
              "64223  photo post portal load fit purpos sure storag ...          2\n",
              "64226  dumb app want post properti rent give option w...          0\n",
              "64227  properti busi got link sm happi perform still ...          2\n",
              "64230  useless app search flat kondapur hyderabad non...          0\n",
              "\n",
              "[37427 rows x 2 columns]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.dropna(inplace=True)\n",
        "df=df.iloc[:,1:3]\n",
        "df.rename(columns = {'Translated_Review':'REVIEW', \n",
        "                       'Sentiment':'SENTIMENT'}, inplace = True)\n",
        "df=classifyCol(df,'SENTIMENT')\n",
        "df=cleanCol(df,'REVIEW')\n",
        "display(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Applying Bag Of Words model to the REVIEW column"
      ],
      "metadata": {
        "id": "ysHdRA3SlYb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=bagOfWords(df['REVIEW'])\n",
        "y=df['SENTIMENT']"
      ],
      "metadata": {
        "id": "tsFMG0YwlVdP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the Dataframe into the Training set and Test set"
      ],
      "metadata": {
        "id": "dqa96DUClQsg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBTKOGQGdHFA",
        "outputId": "97c554c1-5a8d-4055-ace6-61c8dc34fec6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train: 29941\n",
            "x_test: 7486\n",
            "y_train: 29941\n",
            "y_test: 7486\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.20, random_state = 0)\n",
        "\n",
        "print(\"x_train:\",len(x_train))\n",
        "print(\"x_test:\",len(x_test))\n",
        "print(\"y_train:\",len(y_train))\n",
        "print(\"y_test:\",len(y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Applying Random Forest Classification model"
      ],
      "metadata": {
        "id": "k8Z2YT3sl1hH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "RFC_model = RandomForestClassifier(n_estimators = 75, criterion = 'entropy', random_state = 0)\n",
        "RFC_model.fit(x_train, y_train)\n",
        "getAcc(RFC_model, x_train, x_test, y_train, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2kuYuDlRQfDM",
        "outputId": "59d02190-a157-4b3b-dd0e-295519f2521a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 90.11488111140797\n",
            "R2 Score: 0.6030273213438854\n",
            "Mean Absolute Error: 0.15749398877905424\n",
            "Confusion Matrix:\n",
            " [[1235   58  394]\n",
            " [  16  900  144]\n",
            " [  45   83 4611]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.73      0.83      1687\n",
            "           1       0.86      0.85      0.86      1060\n",
            "           2       0.90      0.97      0.93      4739\n",
            "\n",
            "    accuracy                           0.90      7486\n",
            "   macro avg       0.90      0.85      0.87      7486\n",
            "weighted avg       0.90      0.90      0.90      7486\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving the Model having highest Accuracy"
      ],
      "metadata": {
        "id": "oPpparQFmQFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#pickle.dump(LR_model, open('LR_model.pkl','wb')) \n",
        "model = pickle.load(open('model.pkl','rb'))"
      ],
      "metadata": {
        "id": "wVv8Jo9KqUyg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preparing dataframe to apply the saved Model"
      ],
      "metadata": {
        "id": "v-80W9lYgn-H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('googleplaystore_user_reviews.csv')\n",
        "display(df)\n",
        "\n",
        "df.dropna(inplace=True)\n",
        "df=df.iloc[:,1:3]\n",
        "df.rename(columns = {'Translated_Review':'REVIEW', \n",
        "                       'Sentiment':'SENTIMENT'}, inplace = True)\n",
        "df=classifyCol(df,'SENTIMENT')\n",
        "df=cleanCol(df,'REVIEW')\n",
        "display(df)\n",
        "\n",
        "x=bagOfWords(df['REVIEW'])\n",
        "y=df['SENTIMENT']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "pWro3YYMSo46",
        "outputId": "3b274da9-7b75-4169-eda2-3279fdfa7332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6ea9f8d9-41f3-45e1-b0df-87b03744e9c7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th>App</th>\n",
              "      <th>Translated_Review</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Sentiment_Polarity</th>\n",
              "      <th>Sentiment_Subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>I like eat delicious food. That's I'm cooking ...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.533333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10 Best Foods for You</td>\n",
              "      <td>This help eating healthy exercise regular basis</td>\n",
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              "      <td>0.25</td>\n",
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              "      <th>64294</th>\n",
              "      <td>Houzz Interior Design Ideas</td>\n",
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              "      <td>NaN</td>\n",
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              "  </tbody>\n",
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              "<p>64295 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ea9f8d9-41f3-45e1-b0df-87b03744e9c7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-6ea9f8d9-41f3-45e1-b0df-87b03744e9c7');\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                               App  ... Sentiment_Subjectivity\n",
              "0            10 Best Foods for You  ...               0.533333\n",
              "1            10 Best Foods for You  ...               0.288462\n",
              "2            10 Best Foods for You  ...                    NaN\n",
              "3            10 Best Foods for You  ...               0.875000\n",
              "4            10 Best Foods for You  ...               0.300000\n",
              "...                            ...  ...                    ...\n",
              "64290  Houzz Interior Design Ideas  ...                    NaN\n",
              "64291  Houzz Interior Design Ideas  ...                    NaN\n",
              "64292  Houzz Interior Design Ideas  ...                    NaN\n",
              "64293  Houzz Interior Design Ideas  ...                    NaN\n",
              "64294  Houzz Interior Design Ideas  ...                    NaN\n",
              "\n",
              "[64295 rows x 5 columns]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For SENTIMENT column:\n",
            "{'Negative': 0, 'Neutral': 1, 'Positive': 2}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
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              "      <th>REVIEW</th>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-27b38b65-5376-4193-846a-613cd792a8f0');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                  REVIEW  SENTIMENT\n",
              "0      like eat delici food cook food case best food ...          2\n",
              "1                  help eat healthi exercis regular basi          2\n",
              "3                     work great especi go groceri store          2\n",
              "4                                           best idea us          2\n",
              "5                                               best way          2\n",
              "...                                                  ...        ...\n",
              "64222  ad older mani agent not much owner post detail...          2\n",
              "64223  photo post portal load fit purpos sure storag ...          2\n",
              "64226  dumb app want post properti rent give option w...          0\n",
              "64227  properti busi got link sm happi perform still ...          2\n",
              "64230  useless app search flat kondapur hyderabad non...          0\n",
              "\n",
              "[37427 rows x 2 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing the Original output with the Predicted output"
      ],
      "metadata": {
        "id": "dU7IQsAjkXD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=model.predict(x)\n",
        "\n",
        "df['PREDICTED SENTIMENT']=y_pred\n",
        "df.rename(columns = {'SENTIMENT':'ORIGINAL SENTIMENT'}, inplace = True)\n",
        "df['ORIGINAL SENTIMENT'].replace({0: 'Negative', \n",
        "                                  1: 'Neutral', \n",
        "                                  2: 'Positive'}, inplace=True)\n",
        "df['PREDICTED SENTIMENT'].replace({0: 'Negative', \n",
        "                                   1: 'Neutral', \n",
        "                                   2: 'Positive'}, inplace=True)\n",
        "\n",
        "display(df.sample(10))"
      ],
      "metadata": {
        "id": "y9m8Um9whX0X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "3c85b6ac-1f42-49d4-97b7-6138dd8e4e2c"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "      <th>REVIEW</th>\n",
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              "      <td>Positive</td>\n",
              "      <td>Positive</td>\n",
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              "    <tr>\n",
              "      <th>16554</th>\n",
              "      <td>koskoca netflix give free trial period month y...</td>\n",
              "      <td>Positive</td>\n",
              "      <td>Positive</td>\n",
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              "    <tr>\n",
              "      <th>3695</th>\n",
              "      <td>transact attach facil go may upload doc take t...</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>Neutral</td>\n",
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              "    <tr>\n",
              "      <th>49636</th>\n",
              "      <td>like everi say stream anyth video blank live s...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>Positive</td>\n",
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              "                                                     [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",
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            "text/plain": [
              "                                                  REVIEW  ... PREDICTED SENTIMENT\n",
              "57807   grubhub almost everi day driver custom top notch  ...            Positive\n",
              "12317  love pet inform onr place easi access even rec...  ...            Positive\n",
              "35813                                               nice  ...            Positive\n",
              "44960                                               fake  ...            Negative\n",
              "20249  horribl app approach date random profil day ze...  ...            Negative\n",
              "51847  english pleas pleas provid option chang langua...  ...            Positive\n",
              "32503   love far fan age say fast pace keep get distract  ...            Positive\n",
              "16554  koskoca netflix give free trial period month y...  ...            Positive\n",
              "3695   transact attach facil go may upload doc take t...  ...             Neutral\n",
              "49636  like everi say stream anyth video blank live s...  ...            Positive\n",
              "\n",
              "[10 rows x 3 columns]"
            ]
          },
          "metadata": {}
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  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "NLPmodel.ipynb",
      "provenance": [],
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    "kernelspec": {
      "display_name": "Python 3",
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      "name": "python"
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    "accelerator": "TPU"
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  "nbformat_minor": 0
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