ALBERT_Experiments_Fake_review_detection (1).ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "device_list = tf.test.gpu_device_name()\n",
        "device_list"
      ],
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        "colab": {
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            "text/plain": [
              "'/device:GPU:0'"
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          "execution_count": 1
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      "cell_type": "markdown",
      "id": "b948b5a6",
      "metadata": {
        "id": "b948b5a6"
      },
      "source": [
        "## Reading the data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "80cMNpPJZrTc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f2bdfbd-bfb4-4568-8353-05b604bd9096"
      },
      "id": "80cMNpPJZrTc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "90488eb7",
      "metadata": {
        "id": "90488eb7"
      },
      "source": [
        "https://github.com/aayush210789/Deception-Detection-on-Amazon-reviews-dataset/blob/master/amazon_reviews.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "69c3882c",
      "metadata": {
        "id": "69c3882c"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import re\n",
        "\n",
        "# Importing sklearn.metrics libraries\n",
        "from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# Libraries required for hyper-parameter tuning\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import KFold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bc3bced4",
      "metadata": {
        "id": "bc3bced4"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/MS-Thesis backup/datasets/amazon_reviews.txt\", delimiter = \"\\t\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68a98586",
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              "   DOC_ID       LABEL  RATING VERIFIED_PURCHASE PRODUCT_CATEGORY  PRODUCT_ID  \\\n",
              "0       1  __label1__       4                 N               PC  B00008NG7N   \n",
              "1       2  __label1__       4                 Y         Wireless  B00LH0Y3NM   \n",
              "2       3  __label1__       3                 N             Baby  B000I5UZ1Q   \n",
              "3       4  __label1__       4                 N  Office Products  B003822IRA   \n",
              "4       5  __label1__       4                 N           Beauty  B00PWSAXAM   \n",
              "\n",
              "                                       PRODUCT_TITLE  \\\n",
              "0        Targus PAUK10U Ultra Mini USB Keypad, Black   \n",
              "1  Note 3 Battery : Stalion Strength Replacement ...   \n",
              "2       Fisher-Price Papasan Cradle Swing, Starlight   \n",
              "3  Casio MS-80B Standard Function Desktop Calculator   \n",
              "4  Shine Whitening - Zero Peroxide Teeth Whitenin...   \n",
              "\n",
              "               REVIEW_TITLE                                        REVIEW_TEXT  \n",
              "0                    useful  When least you think so, this product will sav...  \n",
              "1     New era for batteries  Lithium batteries are something new introduced...  \n",
              "2  doesn't swing very well.  I purchased this swing for my baby. She is 6 m...  \n",
              "3          Great computing!  I was looking for an inexpensive desk calcolat...  \n",
              "4     Only use twice a week  I only use it twice a week and the results are...  "
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              "      <td>N</td>\n",
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              "      <td>Casio MS-80B Standard Function Desktop Calculator</td>\n",
              "      <td>Great computing!</td>\n",
              "      <td>I was looking for an inexpensive desk calcolat...</td>\n",
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              "      <th>4</th>\n",
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              "      <td>N</td>\n",
              "      <td>Beauty</td>\n",
              "      <td>B00PWSAXAM</td>\n",
              "      <td>Shine Whitening - Zero Peroxide Teeth Whitenin...</td>\n",
              "      <td>Only use twice a week</td>\n",
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      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c23afd3a",
      "metadata": {
        "colab": {
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        },
        "id": "c23afd3a",
        "outputId": "e048ee89-13e2-4c8b-e614-e1e15e160827"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21000, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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      "source": [
        "data.shape"
      ]
    },
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      "execution_count": null,
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        "id": "ffd821f3",
        "outputId": "1bf2eb55-ee70-4dce-9165-4a6290aa15ee"
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21000 entries, 0 to 20999\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   DOC_ID             21000 non-null  int64 \n",
            " 1   LABEL              21000 non-null  object\n",
            " 2   RATING             21000 non-null  int64 \n",
            " 3   VERIFIED_PURCHASE  21000 non-null  object\n",
            " 4   PRODUCT_CATEGORY   21000 non-null  object\n",
            " 5   PRODUCT_ID         21000 non-null  object\n",
            " 6   PRODUCT_TITLE      21000 non-null  object\n",
            " 7   REVIEW_TITLE       21000 non-null  object\n",
            " 8   REVIEW_TEXT        21000 non-null  object\n",
            "dtypes: int64(2), object(7)\n",
            "memory usage: 1.4+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "470064da",
      "metadata": {
        "id": "470064da"
      },
      "source": [
        "## Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "88b2459a",
      "metadata": {
        "id": "88b2459a"
      },
      "outputs": [],
      "source": [
        "#Dropping unnecessary columns\n",
        "data.drop(['DOC_ID','PRODUCT_ID'], axis=1, inplace=True) #The IDs in this case do not provide any useful information, hence dropping them"
      ]
    },
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          "data": {
            "text/plain": [
              "__label1__    10500\n",
              "__label2__    10500\n",
              "Name: LABEL, dtype: int64"
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          "execution_count": 9
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      "source": [
        "data.LABEL.value_counts()"
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      "id": "329dfeca",
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      "source": [
        "# Renaming the Label into numeric\n",
        "data.loc[data[\"LABEL\"] == \"__label1__\", \"LABEL\"] = '1'\n",
        "data.loc[data[\"LABEL\"] == \"__label2__\", \"LABEL\"] = '0'"
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              "  LABEL  RATING VERIFIED_PURCHASE PRODUCT_CATEGORY  \\\n",
              "0     1       4                 N               PC   \n",
              "\n",
              "                                 PRODUCT_TITLE REVIEW_TITLE  \\\n",
              "0  Targus PAUK10U Ultra Mini USB Keypad, Black       useful   \n",
              "\n",
              "                                         REVIEW_TEXT  \n",
              "0  When least you think so, this product will sav...  "
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d6737914-4566-4e8e-8e5e-895d6d59c80b 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-d6737914-4566-4e8e-8e5e-895d6d59c80b');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ec6af24",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ec6af24",
        "outputId": "d2ede509-0ff4-4d14-9554-5b03c98ac7b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21000 entries, 0 to 20999\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   LABEL              21000 non-null  object\n",
            " 1   RATING             21000 non-null  int64 \n",
            " 2   VERIFIED_PURCHASE  21000 non-null  object\n",
            " 3   PRODUCT_CATEGORY   21000 non-null  object\n",
            " 4   PRODUCT_TITLE      21000 non-null  object\n",
            " 5   REVIEW_TITLE       21000 non-null  object\n",
            " 6   REVIEW_TEXT        21000 non-null  object\n",
            "dtypes: int64(1), object(6)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94a96a2f",
      "metadata": {
        "id": "94a96a2f"
      },
      "outputs": [],
      "source": [
        "data['LABEL'] = data['LABEL'].astype(int)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1df0edc"
      },
      "source": [
        "## EDA"
      ],
      "id": "c1df0edc"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21965167"
      },
      "source": [
        "##### Univariate Analysis"
      ],
      "id": "21965167"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8b855494",
        "outputId": "21b4fab8-7f26-4346-c97c-34fed680ccbf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "values=data['LABEL'].value_counts()\n",
        "data.LABEL.value_counts().plot(kind='pie', startangle = 90,autopct= lambda x: '{:.0f}'.format(x*values.sum()/100));"
      ],
      "id": "8b855494"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b2e6d03c",
        "outputId": "d22af276-b6fc-4808-9893-02d344cc73d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data.RATING.value_counts().plot(kind='bar')\n",
        "plt.xlabel('Ratings')\n",
        "plt.ylabel('Count');"
      ],
      "id": "b2e6d03c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "878d1a34"
      },
      "outputs": [],
      "source": [
        "def addlabels(x,y):\n",
        "    for i in range(len(x)):\n",
        "        plt.text(i,y[i],y[i])"
      ],
      "id": "878d1a34"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "673dc41f",
        "outputId": "da4a8347-6b86-460a-a2dc-44018e67b6df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEECAYAAADpigmnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUrUlEQVR4nO3df7BfdX3n8eeroaj4AxCyDE1wk11Su5HWLl6Q6tpRcCBANbRFisOUaKPZbanVdYcKq5Wu6FSHrhRbtc0C5ccwYEpxoKs1RkTddQVJEEWgLFmQkixKSgC3upUNvveP7yfyNd4kN5/kfr+5uc/HzJ17zvt8zjnvM5PJ655f32+qCkmSevzUuBuQJM1chogkqZshIknqZohIkroZIpKkboaIJKnbfuNuYNQOPfTQWrBgwbjbkKQZZd26df9QVXO3rU9biCS5HPgV4NGqOqrVLgJeBzwF/C/gzVX1RFt2PrAceBr4vapa3epLgEuAOcClVfXBVl8IXAccAqwDfrOqntpZXwsWLGDt2rV78lAlaZ+X5KHJ6tN5OesKYMk2tTXAUVX1C8D/BM5vzS0GzgRe0tb5WJI5SeYAHwVOBhYDb2xjAT4EXFxVRwKPMwggSdIITVuIVNWXgM3b1D5bVVva7K3A/Da9FLiuqn5QVQ8C64Fj28/6qnqgnWVcByxNEuB44Pq2/pXAadN1LJKkyY3zxvpvAX/bpucBDw8t29Bq26sfAjwxFEhb65KkERpLiCR5N7AFuGZE+1uRZG2StZs2bRrFLiVpVhh5iCR5E4Mb7mfVM5/+uBE4YmjY/FbbXv0x4KAk+21Tn1RVrayqiaqamDv3Jx4ukCR1GmmItCetfh94fVV9f2jRTcCZSZ7VnrpaBHwVuB1YlGRhkv0Z3Hy/qYXPLcDpbf1lwI2jOg5J0sC0hUiSa4GvAC9OsiHJcuDPgOcDa5LcmeTPAarqbmAVcA/wGeCcqnq63fP4XWA1cC+wqo0FeBfwziTrGdwjuWy6jkWSNLnMtu8TmZiYKN8TkaRdk2RdVU1sW591b6zPFAvO+9S4W9hnfOuDp467BWmf5WdnSZK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqZshIknqZohIkroZIpKkboaIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqZshIknqZohIkroZIpKkboaIJKmbISJJ6maISJK6GSKSpG7TFiJJLk/yaJJvDtVemGRNkvvb74NbPUk+kmR9km8kOXponWVt/P1Jlg3VX5bkrrbOR5Jkuo5FkjS56TwTuQJYsk3tPODmqloE3NzmAU4GFrWfFcDHYRA6wAXAy4FjgQu2Bk8b89ah9bbdlyRpmk1biFTVl4DN25SXAle26SuB04bqV9XArcBBSQ4HTgLWVNXmqnocWAMsacteUFW3VlUBVw1tS5I0IqO+J3JYVT3Spr8NHNam5wEPD43b0Go7qm+YpD6pJCuSrE2ydtOmTbt3BJKkHxnbjfV2BlEj2tfKqpqoqom5c+eOYpeSNCuMOkS+0y5F0X4/2uobgSOGxs1vtR3V509SlySN0KhD5CZg6xNWy4Abh+pnt6e0jgOebJe9VgMnJjm43VA/EVjdln03yXHtqayzh7YlSRqR/aZrw0muBV4NHJpkA4OnrD4IrEqyHHgIOKMN/zRwCrAe+D7wZoCq2pzkQuD2Nu59VbX1Zv3vMHgC7DnA37YfSdIITVuIVNUbt7PohEnGFnDOdrZzOXD5JPW1wFG706Mkaff4xrokqZshIknqZohIkroZIpKkboaIJKmbISJJ6maISJK6Tdt7IpL2TQvO+9S4W9infOuDp467hd3imYgkqZshIknqZohIkroZIpKkboaIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqZshIknqZohIkroZIpKkboaIJKmbISJJ6jaWEEny75PcneSbSa5N8uwkC5PclmR9kk8k2b+NfVabX9+WLxjazvmtfl+Sk8ZxLJI0m408RJLMA34PmKiqo4A5wJnAh4CLq+pI4HFgeVtlOfB4q1/cxpFkcVvvJcAS4GNJ5ozyWCRpthvX5az9gOck2Q84AHgEOB64vi2/EjitTS9t87TlJyRJq19XVT+oqgeB9cCxI+pfksQYQqSqNgJ/DPw9g/B4ElgHPFFVW9qwDcC8Nj0PeLitu6WNP2S4Psk6kqQRGMflrIMZnEUsBH4GeC6Dy1HTuc8VSdYmWbtp06bp3JUkzSrjuJz1WuDBqtpUVf8PuAF4JXBQu7wFMB/Y2KY3AkcAtOUHAo8N1ydZ58dU1cqqmqiqiblz5+7p45GkWWscIfL3wHFJDmj3Nk4A7gFuAU5vY5YBN7bpm9o8bfnnq6pa/cz29NZCYBHw1REdgySJwQ3ukaqq25JcD9wBbAG+BqwEPgVcl+T9rXZZW+Uy4Ook64HNDJ7IoqruTrKKQQBtAc6pqqdHejCSNMuNPEQAquoC4IJtyg8wydNVVfVPwBu2s50PAB/Y4w1KkqbEN9YlSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktRtSiGS5JVTqUmSZpepnon86RRrkqRZZIffbJjkl4BXAHOTvHNo0QuAOdPZmCRp77ezr8fdH3heG/f8ofp3gdOnqylJ0sywwxCpqi8CX0xyRVU9NKKeJEkzxM7ORLZ6VpKVwILhdarq+OloSpI0M0w1RP4K+HPgUuDp6WtHkjSTTDVEtlTVx6e1E0nSjDPVR3z/JsnvJDk8yQu3/kxrZ5Kkvd5Uz0SWtd/nDtUK+Bd7th1J0kwypRCpqoXT3YgkaeaZUogkOXuyelVd1bPTJAcxuEl/FIMzmt8C7gM+weAJsG8BZ1TV40kCXAKcAnwfeFNV3dG2swx4T9vs+6vqyp5+JEl9pnpP5Jihn1cBfwi8fjf2ewnwmar6OeClwL3AecDNVbUIuLnNA5wMLGo/K4CPA7R7MhcALweOBS5IcvBu9CRJ2kVTvZz1tuH5diZxXc8OkxwI/DLwprbtp4CnkiwFXt2GXQl8AXgXsBS4qqoKuDXJQUkOb2PXVNXmtt01wBLg2p6+JEm7rvej4L8H9N4nWQhsAv4yydeSXJrkucBhVfVIG/Nt4LA2PQ94eGj9Da22vbokaUSmek/kbxjcu4DBBy/+K2DVbuzzaOBtVXVbkkt45tIVAFVVSWrStTskWcHgUhgvetGL9tRmJWnWm+ojvn88NL0FeKiqNnTucwOwoapua/PXMwiR7yQ5vKoeaZerHm3LNwJHDK0/v9U28szlr631L0y2w6paCawEmJiY2GPhJEmz3ZQuZ7UPYvw7Bp/kezDwVO8Oq+rbwMNJXtxKJwD3ADfxzPsoy4Ab2/RNwNkZOA54sl32Wg2cmOTgdkP9xFaTJI3IVC9nnQFcxOAv/QB/muTcqrq+c79vA65Jsj/wAPBmBoG2Ksly4CHgjDb20wwe713P4BHfNwNU1eYkFwK3t3Hv23qTXZI0GlO9nPVu4JiqehQgyVzgcwwuRe2yqroTmJhk0QmTjC3gnO1s53Lg8p4eJEm7b6pPZ/3U1gBpHtuFdSVJ+6ipnol8JslqnnkH4zcYXGaSJM1iO/uO9SMZvL9xbpJfA/5NW/QV4Jrpbk6StHfb2ZnInwDnA1TVDcANAEl+vi173bR2J0naq+3svsZhVXXXtsVWWzAtHUmSZoydhchBO1j2nD3ZiCRp5tlZiKxN8tZti0neAqybnpYkSTPFzu6JvAP4ZJKzeCY0JoD9gV+dzsYkSXu/HYZIVX0HeEWS1zD4AimAT1XV56e9M0nSXm+q3ydyC3DLNPciSZphfOtcktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUbWwhkmROkq8l+a9tfmGS25KsT/KJJPu3+rPa/Pq2fMHQNs5v9fuSnDSeI5Gk2WucZyJvB+4dmv8QcHFVHQk8Dixv9eXA461+cRtHksXAmcBLgCXAx5LMGVHvkiTGFCJJ5gOnApe2+QDHA9e3IVcCp7XppW2etvyENn4pcF1V/aCqHgTWA8eO5ggkSTC+M5E/AX4f+GGbPwR4oqq2tPkNwLw2PQ94GKAtf7KN/1F9knV+TJIVSdYmWbtp06Y9eRySNKuNPESS/ArwaFWtG9U+q2plVU1U1cTcuXNHtVtJ2uftN4Z9vhJ4fZJTgGcDLwAuAQ5Ksl8725gPbGzjNwJHABuS7AccCDw2VN9qeB1J0giM/Eykqs6vqvlVtYDBjfHPV9VZwC3A6W3YMuDGNn1Tm6ct/3xVVauf2Z7eWggsAr46osOQJDGeM5HteRdwXZL3A18DLmv1y4Crk6wHNjMIHqrq7iSrgHuALcA5VfX06NuWpNlrrCFSVV8AvtCmH2CSp6uq6p+AN2xn/Q8AH5i+DiVJO+Ib65KkboaIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqZshIknqZohIkroZIpKkboaIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqZshIknqZohIkroZIpKkboaIJKnbyEMkyRFJbklyT5K7k7y91V+YZE2S+9vvg1s9ST6SZH2SbyQ5emhby9r4+5MsG/WxSNJsN44zkS3Af6iqxcBxwDlJFgPnATdX1SLg5jYPcDKwqP2sAD4Og9ABLgBeDhwLXLA1eCRJozHyEKmqR6rqjjb9f4B7gXnAUuDKNuxK4LQ2vRS4qgZuBQ5KcjhwErCmqjZX1ePAGmDJCA9Fkma9sd4TSbIA+NfAbcBhVfVIW/Rt4LA2PQ94eGi1Da22vbokaUTGFiJJngf8NfCOqvru8LKqKqD24L5WJFmbZO2mTZv21GYladYbS4gk+WkGAXJNVd3Qyt9pl6lovx9t9Y3AEUOrz2+17dV/QlWtrKqJqpqYO3funjsQSZrlxvF0VoDLgHur6sNDi24Ctj5htQy4cah+dntK6zjgyXbZazVwYpKD2w31E1tNkjQi+41hn68EfhO4K8mdrfYfgQ8Cq5IsBx4CzmjLPg2cAqwHvg+8GaCqNie5ELi9jXtfVW0ezSFIkmAMIVJV/x3IdhafMMn4As7ZzrYuBy7fc91JknaFb6xLkroZIpKkboaIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqZshIknqZohIkroZIpKkboaIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqZshIknqZohIkroZIpKkboaIJKnbjA+RJEuS3JdkfZLzxt2PJM0mMzpEkswBPgqcDCwG3phk8Xi7kqTZY0aHCHAssL6qHqiqp4DrgKVj7kmSZo39xt3AbpoHPDw0vwF4+baDkqwAVrTZf0xy3wh6mw0OBf5h3E3sTD407g40Jv773LP++WTFmR4iU1JVK4GV4+5jX5NkbVVNjLsPaTL++xyNmX45ayNwxND8/FaTJI3ATA+R24FFSRYm2R84E7hpzD1J0qwxoy9nVdWWJL8LrAbmAJdX1d1jbms28RKh9mb++xyBVNW4e5AkzVAz/XKWJGmMDBFJUjdDRDuV5NNJFoy7D0l7H0NEU/GXwGeTvDvJT4+7GUl7D2+sa0qSPA/4A2AJcDXww63LqurD4+pLAkjy3h0srqq6cGTNzDIz+hFfjdRTwPeAZwHPZyhEpL3A9yapHQC8BTgEMESmiWci2qkkS4APM3iR831V9f0xtyRtV5LnA28HlgOrgP9cVY+Ot6t9l2cimop3A2/wRU7tzZK8EHgncBZwJXB0VT0+3q72fYaIdqqqXjXuHqQdSXIR8GsM3lL/+ar6xzG3NGt4OUvSjJfkh8APgC3A8H9qYXBj/QVjaWwWMEQkSd18T0SS1M0QkSR1M0S0T0tyS5KTtqm9I8nHd2Eb70vy2jb9qiR3J7kzybwk1+9iP1ckOX079Qfbdu9I8ku7st3t7GtBkm/u7nakHTFEtK+7lsGXlQ07s9V3KsmcqnpvVX2ulc4C/qiqfrGqNlbVTwTCbji3qn4ROA/4i6mulMSnLDU2hoj2ddcDp7ZvvqR9kOTPAP8tyYlJvtL+8v+r9tEuJPlWkg8luQN4w9azhyRvAc4ALkxyzfBf+knmJLkoye1JvpHk37Z6kvxZkvuSfA74Z1Po+UvAkUO9HNqmJ5J8oU3/YZKrk3wZuDrJYUk+meTr7ecVbVtzkvyXdvb02STPaeu/tfX69SR/neSAVn9Dkm+2+pd2dGwSGCLax1XVZuCrwMmtdCaDt5gPAd4DvLaqjgbWMnhRbavHquroqrpuaFuXMnhr/9yqOmubXS0HnqyqY4BjgLcmWQj8KvBiYDFwNvAKdu51wF1TGLe49f9G4CPAF6vqpcDRwNYXQxcBH62qlwBPAL/e6jdU1TFt/L2tf4D3Aie1+ut3cmySLxtqVth6SevG9ns5cByD/4S/nARgf+ArQ+t8Yhf3cSLwC0P3Ow5k8B/4LwPXVtXTwP9O8vkdbOOiJO8BNvHMf+o7clNV/d82fTyDkKLt68kkBwMPVtWdbcw6YEGbPirJ+4GDgOcx+IppgC8DVyRZBdywk2N7cAo9ah9niGg2uBG4OMnRwAFVtS7J64A17a/4yUz2gX47EuBtVbX6x4rJKbuwjXOratsb9Vt45orBs7dZNpUefzA0/TTwnDZ9BXBaVX09yZuAVwNU1b9L8nLgVGBdkpexnWOTwMtZmgXaR2DcAlzOMzfUbwVemWTrvYfnJvnZ3djNauC3t37fSpKfTfJcBvc3fqPdVzgceM0ubvdbwMva9K/vYNzNwG+3fc9JcuBOtvt84JHW748uzSX5l1V1W1W9l8EZ0RE7ODbJENGscS3w0vabqtoEvAm4Nsk3GFzK+rnd2P6lwD3AHe1m+18wONP/JHB/W3YVP37JbCr+E3BJkrUMziS25+3Aa5LcxeCy1eKdbPcPgNsYXL76u6H6RUnuasfwP4Cv7+DYJD/2RJLUzzMRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEnd/j+BB6Kcru/mDwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data['VERIFIED_PURCHASE'].value_counts().plot(kind='bar',)\n",
        "plt.xlabel('Verified Purchase')\n",
        "plt.ylabel('Count');"
      ],
      "id": "673dc41f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a5107ee9",
        "outputId": "dcba6238-c0f0-404d-8ac6-e94f680618c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,15))\n",
        "data.PRODUCT_CATEGORY.value_counts().plot(kind = 'pie', fontsize = 8.5)\n",
        "plt.ylabel(' ');"
      ],
      "id": "a5107ee9"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55b8e3a3"
      },
      "source": [
        "##### Inference\n",
        "All the categories have equal number of reviews"
      ],
      "id": "55b8e3a3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9b43f3f"
      },
      "source": [
        "#### Bi-variate analysis"
      ],
      "id": "c9b43f3f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2a806b50",
        "outputId": "3d543ab6-4de1-4b30-b578-369dbdce6b23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.countplot(x='LABEL', hue='VERIFIED_PURCHASE', data=data)\n",
        "plt.title('Label Vs Verified Purchase');"
      ],
      "id": "2a806b50"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3b5ecaa"
      },
      "source": [
        "##### Inference:\n",
        "Most fake reviews are from customers who have not purchased the product on the website"
      ],
      "id": "c3b5ecaa"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e734403e",
        "outputId": "eb102a67-e881-4204-df39-ce4c2786de7d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.countplot(x='LABEL', hue='RATING', data=data);"
      ],
      "id": "e734403e"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "196f8511"
      },
      "source": [
        "##### Inference:\n",
        "- Ratings are divided equally for both the labels\n",
        "- Both fake and real reviews have high/positive ratings"
      ],
      "id": "196f8511"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1273eaeb",
        "outputId": "00531939-e4b9-4071-9033-1ea1c35ffa89",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(25,15))\n",
        "sns.countplot(x='LABEL', hue='PRODUCT_CATEGORY', data=data);"
      ],
      "id": "1273eaeb"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20f76970"
      },
      "source": [
        "##### Inference\n",
        "Equal number of product reviews for both the labels"
      ],
      "id": "20f76970"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2e37fd18"
      },
      "source": [
        "## Analysis"
      ],
      "id": "2e37fd18"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc234d9c"
      },
      "source": [
        "#####  1. Visualising the sentiment of both fake and real reviews using TextBlob"
      ],
      "id": "fc234d9c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f233ce63"
      },
      "outputs": [],
      "source": [
        "from textblob import TextBlob\n",
        "data['sentiment_score'] = data['REVIEW_TEXT'].apply(lambda x: TextBlob(x).sentiment.polarity)"
      ],
      "id": "f233ce63"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "88606eab"
      },
      "outputs": [],
      "source": [
        "def label_column(x):\n",
        "    if x == 0:\n",
        "        return 'Neutral'\n",
        "    elif x < 0:\n",
        "        return 'Negative'\n",
        "    else:\n",
        "        return 'Positive'"
      ],
      "id": "88606eab"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "25fb4395",
        "outputId": "23d4db5e-e781-473a-9a7c-d8cad09332c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   LABEL  RATING VERIFIED_PURCHASE PRODUCT_CATEGORY  \\\n",
              "0      1       4                 N               PC   \n",
              "1      1       4                 Y         Wireless   \n",
              "2      1       3                 N             Baby   \n",
              "3      1       4                 N  Office Products   \n",
              "4      1       4                 N           Beauty   \n",
              "\n",
              "                                       PRODUCT_TITLE  \\\n",
              "0        Targus PAUK10U Ultra Mini USB Keypad, Black   \n",
              "1  Note 3 Battery : Stalion Strength Replacement ...   \n",
              "2       Fisher-Price Papasan Cradle Swing, Starlight   \n",
              "3  Casio MS-80B Standard Function Desktop Calculator   \n",
              "4  Shine Whitening - Zero Peroxide Teeth Whitenin...   \n",
              "\n",
              "               REVIEW_TITLE  \\\n",
              "0                    useful   \n",
              "1     New era for batteries   \n",
              "2  doesn't swing very well.   \n",
              "3          Great computing!   \n",
              "4     Only use twice a week   \n",
              "\n",
              "                                         REVIEW_TEXT  sentiment_score  \\\n",
              "0  When least you think so, this product will sav...        -0.300000   \n",
              "1  Lithium batteries are something new introduced...         0.219226   \n",
              "2  I purchased this swing for my baby. She is 6 m...         0.347143   \n",
              "3  I was looking for an inexpensive desk calcolat...        -0.088542   \n",
              "4  I only use it twice a week and the results are...         0.145833   \n",
              "\n",
              "  sentiment_label  \n",
              "0        Negative  \n",
              "1        Positive  \n",
              "2        Positive  \n",
              "3        Negative  \n",
              "4        Positive  "
            ],
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              "      <th>0</th>\n",
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              "      <td>useful</td>\n",
              "      <td>When least you think so, this product will sav...</td>\n",
              "      <td>-0.300000</td>\n",
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              "      <td>Note 3 Battery : Stalion Strength Replacement ...</td>\n",
              "      <td>New era for batteries</td>\n",
              "      <td>Lithium batteries are something new introduced...</td>\n",
              "      <td>0.219226</td>\n",
              "      <td>Positive</td>\n",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>N</td>\n",
              "      <td>Baby</td>\n",
              "      <td>Fisher-Price Papasan Cradle Swing, Starlight</td>\n",
              "      <td>doesn't swing very well.</td>\n",
              "      <td>I purchased this swing for my baby. She is 6 m...</td>\n",
              "      <td>0.347143</td>\n",
              "      <td>Positive</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>N</td>\n",
              "      <td>Office Products</td>\n",
              "      <td>Casio MS-80B Standard Function Desktop Calculator</td>\n",
              "      <td>Great computing!</td>\n",
              "      <td>I was looking for an inexpensive desk calcolat...</td>\n",
              "      <td>-0.088542</td>\n",
              "      <td>Negative</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>N</td>\n",
              "      <td>Beauty</td>\n",
              "      <td>Shine Whitening - Zero Peroxide Teeth Whitenin...</td>\n",
              "      <td>Only use twice a week</td>\n",
              "      <td>I only use it twice a week and the results are...</td>\n",
              "      <td>0.145833</td>\n",
              "      <td>Positive</td>\n",
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              "          const element = document.querySelector('#df-445dc212-b1b0-4173-98b5-9946776c872f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "data['sentiment_label']=data['sentiment_score'].apply(lambda x:label_column(x))\n",
        "data.head()"
      ],
      "id": "25fb4395"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2dfa2acf",
        "outputId": "75a51550-975e-4117-cc3f-16d5af1bd9fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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xsP2g6pYkSeqKQR5RmwbMBb6S5GdJTk6yGvCSqrqr9bkbeEl7PgW4o2/7Oa1tSe2SJEnLtUEGtQnAZsCXqurPgAd5cpoTgKoqoEZjZ0kOTDI7yey5c+eOxpCSJEljapBBbQ4wp6qubMvn0Atuv21TmrSfv2vr7wTW79t+qLUtqf0pquqkqppZVTMnT548qi9EkiRpLAwsqFXV3cAdSV7VmrYBbgDOA4av3NwX+FZ7fh6wT7v68/XA/W2K9CJg2yRrt4sItm1tkiRJy7UJAx7/g8DXkkwEfgW8m144PCvJAcCvgXe2vhcAOwK3AA+1vlTVPUn+GfhJ63dUVd0z4LolSZLG3ECDWlVdA8xczKptFtO3gIOXMM6XgS+PbnWSJEnd5p0JJEmSOsqgJkmS1FEGNUmSpI4yqEmSJHWUQU2SJKmjDGqSJEkdZVCTJEnqKIOaJElSRxnUJEmSOsqgJkmS1FEGNUmSpI4yqEmSJHWUQU2SJKmjDGqSJEkdZVCTJEnqKIOaJElSRxnUJEmSOmpEQS3JJSNpkyRJ0uiZ8Ewrk0wCXgisl2RtIG3Vi4ApA65NkiRphfaMQQ14L/Bh4KXA1TwZ1P4AnDDAuiRJklZ4zxjUqup44PgkH6yqzy+jmiRJksSzH1EDoKo+n+QNwNT+barq9AHVJUmStMIbUVBL8u/ABsA1wILWXIBBTZIkaUBGFNSAmcDGVVWDLEaSJElPGun3qP0C+JNBFiJJkqSnGukRtfWAG5JcBTw63FhVOw+kKkmSJI04qB05yCIkSZL0dCO96vP7gy5EkiRJTzXSqz4foHeVJ8BEYGXgwap60aAKkyRJWtGN9IjaGsPPkwTYBXj9oIqSJEnSyK/6XKh6vglsN4B6JEmS1Ix06vMdfYsvoPe9ao8MpCJJkiQBI7/q8219z+cDt9Gb/pQkSdKAjPQctXcPuhBJkiQ91YjOUUsylOTcJL9rj68nGRp0cZIkSSuykV5M8BXgPOCl7fHt1iZJkqQBGWlQm1xVX6mq+e1xKjB5gHVJkiSt8EYa1OYleVeSldrjXcC8QRYmSZK0ohtpUNsfeCdwN3AXsBuw34BqkiRJEiP/eo6jgH2r6l6AJOsAn6YX4CRJkjQAIz2i9trhkAZQVfcAfzaYkiRJkgQjD2ovSLL28EI7ojbSo3GSJEl6DkYatj4D/DjJ2W15d+DowZQkSZIkGPmdCU5PMhv4q9b0jqq6YXBlSZIkacTTly2YGc4kSZKWkZGeoyZJkqRlzKAmSZLUUQY1SZKkjhp4UGu3nPpZku+05WlJrkxyS5JZSSa29lXa8i1t/dS+MT7a2m9Kst2ga5YkSeqCZXFE7e+AG/uWPwUcV1UbAvcCB7T2A4B7W/txrR9JNgb2BKYD2wNfTLLSMqhbkiRpTA00qCUZAv4aOLkth95XfJzTupwG7Nqe79KWaeu3af13Ac6sqker6lbgFmDLQdYtSZLUBYM+ovY54B+AJ9ryusB9VTW/Lc8BprTnU4A7ANr6+1v/he2L2UaSJGm5NbCglmQn4HdVdfWg9rHI/g5MMjvJ7Llz5y6LXUqSJA3UII+ovRHYOcltwJn0pjyPB9ZKMvxFu0PAne35ncD6AG39msC8/vbFbLNQVZ1UVTOraubkyZNH/9VIkiQtYwMLalX10aoaqqqp9C4GuLSq9ga+B+zWuu0LfKs9P68t09ZfWlXV2vdsV4VOAzYCrhpU3ZIkSV0x4ltIjaJ/BM5M8kngZ8Aprf0U4N+T3ALcQy/cUVXXJzmL3u2r5gMHV9WCZV+2JEnSsrVMglpVXQZc1p7/isVctVlVjwC7L2H7o4GjB1ehJElS93hnAkmSpI4yqEmSJHWUQU2SJKmjDGqSJEkdZVCTJEnqKIOaJElSRxnUJEmSOsqgJkmS1FEGNUmSpI4yqEmSJHWUQU2SJKmjDGqSJEkdZVCTJEnqKIOaJElSRxnUJEmSOsqgJkmS1FEGNUmSpI4yqEmSJHWUQU2SJKmjDGqSJEkdZVCTJEnqKIOaJElSRxnUJEmSOsqgJkmS1FEGNUmSpI4yqEmSJHWUQU2SJKmjDGqSJEkdZVCTJEnqKIOaJElSRxnUJEmSOsqgJkmS1FEGNUmSpI4yqEmSJHWUQU2SJKmjDGqSJEkdZVCTJEnqKIOaJElSRxnUJEmSOsqgJkmS1FEGNUmSpI4yqEmSJHWUQU2SJKmjDGqSJEkdZVCTJEnqKIOaJElSRxnUJEmSOmpgQS3J+km+l+SGJNcn+bvWvk6Si5Pc3H6u3dqT5N+S3JLk2iSb9Y21b+t/c5J9B1WzJElSlwzyiNp84O+ramPg9cDBSTYGDgMuqaqNgEvaMsAOwEbtcSDwJegFO+AIYCtgS+CI4XAnSZK0PBtYUKuqu6rqp+35A8CNwBRgF+C01u00YNf2fBfg9Oq5AlgryZ8C2wEXV9U9VXUvcDGw/aDqliRJ6oplco5akqnAnwFXAi+pqrvaqruBl7TnU4A7+jab09qW1C5JkrRcG3hQS7I68HXgw1X1h/51VVVAjdJ+DkwyO8nsuXPnjsaQkiRJY2qgQS3JyvRC2teq6hut+bdtSpP283et/U5g/b7Nh1rbktqfoqpOqqqZVTVz8uTJo/tCJEmSxsAgr/oMcApwY1V9tm/VecDwlZv7At/qa9+nXf35euD+NkV6EbBtkrXbRQTbtjZJkqTl2oQBjv1G4G+B65Jc09o+BhwDnJXkAODXwDvbuguAHYFbgIeAdwNU1T1J/hn4Set3VFXdM8C6JUmSOmFgQa2qLgeyhNXbLKZ/AQcvYawvA18eveokSZK6zzsTSJIkdZRBTZIkqaMMapIkSR1lUJMkSeoog5okSVJHGdQkSZI6yqAmSZLUUQY1SZKkjjKoSZIkdZRBTZIkqaMMapIkSR1lUJMkSeoog5okSVJHGdQkSZI6yqAmSZLUUQY1SZKkjjKoSZIkdZRBTZIkqaMmjHUBWj7cftSmY13CuPGyw68b6xIkSeOEQe1ZbP6R08e6hHHh3DXGugJJkpY/Tn1KkiR1lEFNkiSpowxqkiRJHWVQkyRJ6iiDmiRJUkcZ1CRJkjrKoCZJktRRBjVJkqSOMqhJkiR1lEFNkiSpowxqkiRJHWVQkyRJ6iiDmiRJUkcZ1CRJkjrKoCZJktRRBjVJkqSOMqhJkiR1lEFNkiSpowxqkiRJHWVQkyRJ6iiDmiRJUkcZ1CRJkjrKoCZJktRRBjVJkqSOMqhJkiR1lEFNkiSpoyaMdQGSJC0Lm3/k9LEuYVy4+th9xroE9TGoSZKkhW4/atOxLmHceNnh1w18H+Nm6jPJ9kluSnJLksPGuh5JkqRBGxdBLclKwBeAHYCNgb2SbDy2VUmSJA3WuAhqwJbALVX1q6p6DDgT2GWMa5IkSRqo8RLUpgB39C3PaW2SJEnLreXmYoIkBwIHtsU/JrlpLOtZ0bwc1gN+P9Z1jAtHZKwr0HPk+3wp+D4ft3yfL4XRe5+/fEkrxktQuxNYv295qLUtVFUnAScty6L0pCSzq2rmWNchDZLvc60IfJ93y3iZ+vwJsFGSaUkmAnsC541xTZIkSQM1Lo6oVdX8JB8ALgJWAr5cVdePcVmSJEkDNS6CGkBVXQBcMNZ1aImcdtaKwPe5VgS+zzskVTXWNUiSJGkxxss5apIkSSscg9oKKEkl+Uzf8qFJjhzAfj62yPKPRnsf0kgkWZDkmiS/SHJ2khcu5fYvTXJOez4jyY5963b2tnbqitH8fE+yVpL3P8dtb0uy3nPZVk9lUFsxPQq8Yxn8ET0lqFXVGwa8P2lJHq6qGVW1CfAYcNDSbFxVv6mq3driDGDHvnXnVdUxo1eq9LyM5uf7WsBig1qScXOO+3hnUFsxzad3sughi65IMjnJ15P8pD3e2Nd+cZLrk5yc5NfDHwRJvpnk6rbuwNZ2DLBqO4rxtdb2x/bzzCR/3bfPU5PslmSlJMe2/V6b5L0D/01oRfQDYMMk67T37rVJrkjyWoAkf9Het9ck+VmSNZJMbUfjJgJHAXu09Xsk2S/JCUnWbH8XL2jjrJbkjiQrJ9kgyYXt7+QHSV49hq9fy7fn8vl+ZJJD+/r9IslU4Bhgg/ZePzbJW9r79zzghtb3aZ//Gl0GtRXXF4C9k6y5SPvxwHFVtQXwN8DJrf0I4NKqmg6cA7ysb5v9q2pzYCbwoSTrVtVhPHkUY+9F9jELeCdA+4dvG+B84ADg/rbvLYD3JJk2Sq9XGj4KsANwHfAJ4GdV9Vp6R39Pb90OBQ6uqhnAm4GHh7dv9xo+HJjV3tuz+tbdD1wD/EVr2gm4qKoep/cP5wfb38mhwBcH9yqlpf58X5LDgP9u7/WPtLbNgL+rqle25ad9/o/OS9AwD12uoKrqD0lOBz5E3z9EwFuBjZOFt8V4UZLVgTcBb2/bXpjk3r5tPpTk7e35+sBGwLxn2P3/BY5PsgqwPfD/qurhJNsCr00yPMW0Zhvr1uf6OqVm1STXtOc/AE4BrqT3jxVVdWmSdZO8CPgh8Nl2JPgbVTWn7+/h2cwC9gC+R++Lub/Y/n7eAJzdN84qo/CapMV6Dp/vS+Oqqur/TF7az38tJYPaiu1zwE+Br/S1vQB4fVU90t9xSf9QJXkLvT/+ravqoSSXAZOeaadV9Ujrtx29f9TOHB6O3lGHi5b2hUjP4uF2hGyhJb2nq+qYJOfTOw/th0m2Ax5ZbOenOw/4lyTrAJsDlwKrAfctun9pwJbm830+T51he6bP8Af7tnsLS/n5r6Xn1OcKrKruAc6iN+U47LvAB4cXkgz/4/JDnpyu3BZYu7WvCdzb/khfDby+b6zHk6y8hN3PAt5Nb2rpwtZ2EfC+4W2SvDLJas/x5UnP5gfA3rDwH5zftyMRG1TVdVX1KXq3r1v0fLIHgDUWN2BV/bFtczzwnapaUFV/AG5NsnvbV5K8biCvSGqW8vP9NnpTmiTZDBg+5WSJ7/XmmT7/NUoMavoM0H910IeAme0E6xt48uq4TwDbJvkFsDtwN70/4guBCUlupHfi6RV9Y50EXDt8McEivkvvXJ7/bOf9QO98iRuAn7b9/G886qvBORLYPMm19N67+7b2D7eTqa8FHqc3Vd/ve/Smj65Jssdixp0FvKv9HLY3cECSnwPXA7uM3suQlmikn+9fB9ZJcj3wAeCXAFU1j95R5V8kOXYx4z/T579GiXcm0Ii088kWtPuubg18yakcSZIGy6MVGqmXAWe1rx54DHjPGNcjSdJyzyNqkiRJHeU5apIkSR1lUJMkSeoog5okSVJHGdQkSZI6yqAmabmTZEaSHfuWd05y2ID3+ZYkb3iWPk+5+fUS+pzadxu1kex3avveQUnLIYOapOXRDHq3gAKgqs6rqmMGvM+30LunpySNGoOapE5JslqS85P8vH0j+h5JNk/y/SRXJ7koyZ+2vpcl+VSSq5L8Msmbk0wEjgL2GL57QJL9kpzQtjk1yZeSXJHkV+1I2JeT3Jjk1L46tk3y4yQ/TXL28M2rk9yW5BOt/bokr04yld63vB/S9vnmEbzO9yT5SXudX0/ywr7Vb00yu72mnVr/lZIc27a5Nsl7R+c3LqnLDGqSumZ74DdV9bqq2oTebWo+D+xWVZsDXwaO7us/oaq2BD4MHNFuSXY4MKuqZlTVLJ5ubWBr4BB6N1I/DpgObNqmTdcD/hfw1qraDJgN/M++7X/f2r8EHFpVtwEnAse1ff5gBK/zG1W1RVW9DriRp96TcSqwJfDXwIlJJrX191fVFsAWwHuSTEPScs07E0jqmuuAzyT5FPAd4F5gE+DiJAArAXf19f9G+3k1vYAzEt+uqkpyHfDbqroOoN3rcCowBGxM7z6HABOBHy9hn+9YitfWb5MknwTWAlYHLupbd1ZVPQHcnORX9G4Mvy3w2r7z19YENqLdl1HS8smgJqlTquqXSTajd47ZJ4FLgeurauslbPJo+7mAkX+mDW/zRN/z4eUJbayLq2qvUdznok4Fdq2qnyfZj945bsMWvWVMAQE+WFX9gY427SppOeXUp6ROSfJS4KGq+ipwLLAVMDnJ1m39ykmmP8swDwBrPI8yrgDemGTDts/VkrxylPe5BnBXkpWBvRdZt3uSFyTZADEQrjYAAADTSURBVHgFcBO9I27va/1J8sokqy3F/iSNQx5Rk9Q1mwLHJnkCeBx4HzAf+Lcka9L73PoccP0zjPE94LAk1wD/urQFVNXcdpTrjCSrtOb/xTNPM34bOCfJLvSOfD3beWofB64E5raf/SHvduAq4EXAQVX1SJKT6U3L/jS9+di5wK5L9cIkjTvelF2SJKmjnPqUJEnqKKc+JWmUJfknYPdFms+uqqMX11+SlsSpT0mSpI5y6lOSJKmjDGqSJEkdZVCTJEnqKIOaJElSRxnUJEmSOur/A8hrvQfH/7AWAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.countplot(x='sentiment_label', hue='LABEL', data=data)\n",
        "plt.title('Sentiment Vs Label');"
      ],
      "id": "2dfa2acf"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61ffbd53"
      },
      "source": [
        "##### Inference:\n",
        "Most fake reviews have higher positive and negative sentiment indicating the reviewers either wrote very good things about a product or gave negative feedbacks"
      ],
      "id": "61ffbd53"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "04b97eec",
        "outputId": "cd8e4ef0-e31d-4493-bc78-2d656f8ceda3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.countplot(x='sentiment_label', hue='VERIFIED_PURCHASE', data=data);"
      ],
      "id": "04b97eec"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7e48ce7"
      },
      "source": [
        "##### Inference:\n",
        "Customers who have purchased the product have mostly have positive sentiment score"
      ],
      "id": "c7e48ce7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "19444dd5",
        "outputId": "cac5ffe6-0923-4b47-f893-9760fdb95175",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.countplot(x='sentiment_label', hue='RATING', data=data);"
      ],
      "id": "19444dd5"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bedc810"
      },
      "source": [
        "##### Inference\n",
        "Positive sentiment has highest rating as 5"
      ],
      "id": "8bedc810"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6acc18d7"
      },
      "source": [
        "#####  Checking the length of the reviews\n",
        "- longer/shorter reviews have been verified?"
      ],
      "id": "6acc18d7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5acb7412"
      },
      "outputs": [],
      "source": [
        "data['review_length'] = data['REVIEW_TEXT'].map(len)"
      ],
      "id": "5acb7412"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d3e1d81c",
        "outputId": "c6f62a9c-c222-4966-f02c-0630ee90b356",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16188"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "max(data['review_length'])"
      ],
      "id": "d3e1d81c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "55a8371b",
        "outputId": "c7653b9b-dcec-4378-8868-e7e80be8c005",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "min(data['review_length'])"
      ],
      "id": "55a8371b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3cc25a4e",
        "outputId": "ce052a9c-0e12-483d-e37f-63a3498eed6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(x = 'review_length', y = 'LABEL', data=data)\n",
        "plt.title('Review length');"
      ],
      "id": "3cc25a4e"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ff6cb05"
      },
      "source": [
        "##### Inference\n",
        "All fake reviews have length less than 8k words"
      ],
      "id": "6ff6cb05"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a9b62629",
        "outputId": "75870327-6780-4a50-f0a5-ad4fe1c94e67",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LABEL\n",
              "0    428.064571\n",
              "1    316.538857\n",
              "Name: review_length, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "length = data.groupby('LABEL')['review_length'].mean()\n",
        "length.head()"
      ],
      "id": "a9b62629"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3b7f0fb2"
      },
      "source": [
        "##### Inference:\n",
        "Fake reviews are shorter in length"
      ],
      "id": "3b7f0fb2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f931902d",
        "outputId": "078c33e6-0bc3-4d9a-9678-ce5f30089f85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ax = sns.barplot(x=\"VERIFIED_PURCHASE\", y=\"review_length\", hue=\"LABEL\", data=data)"
      ],
      "id": "f931902d"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cb81a824"
      },
      "source": [
        "##### Inference:\n",
        "- Fake reviews are shorter in length in both cases in regardless of product being purchased or not\n",
        "- Real reviews which have not been verified has the highest review length"
      ],
      "id": "cb81a824"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bf7f03fe"
      },
      "source": [
        "##### Inference:\n",
        "Fake reviews with neutral tone have higher review length.\n",
        "Real reviews have highest positive and negative sentiment\n"
      ],
      "id": "bf7f03fe"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "11696c8e",
        "outputId": "e5d924c3-e898-4e90-cb38-5f0c46eb36d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ax = sns.barplot(x=\"RATING\", y=\"review_length\", hue=\"LABEL\", data=data)"
      ],
      "id": "11696c8e"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ALBERT"
      ],
      "metadata": {
        "id": "V3rTLkYaPaSA"
      },
      "id": "V3rTLkYaPaSA"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-X_d539JX-_",
        "outputId": "d96b4463-7e23-45b8-e42c-a2cc242da3df"
      },
      "id": "K-X_d539JX-_",
      "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",
            "Collecting transformers\n",
            "  Downloading transformers-4.21.2-py3-none-any.whl (4.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.7 MB 7.6 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.9.1-py3-none-any.whl (120 kB)\n",
            "\u001b[K     |████████████████████████████████| 120 kB 93.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 68.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.12.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.9.1 tokenizers-0.12.1 transformers-4.21.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['VERIFIED_PURCHASE'].replace('Y','YES', inplace=True)\n",
        "data['VERIFIED_PURCHASE'].replace('N','NO',inplace=True)\n",
        "\n",
        "def rating(x):\n",
        "    if x == 5:\n",
        "        return 'Very Good'\n",
        "    elif x == 4:\n",
        "        return 'Good'\n",
        "    elif x == 3:\n",
        "        return 'Avg'\n",
        "    elif x == 2:\n",
        "        return 'Poor'\n",
        "    else:\n",
        "        return 'Bad'\n",
        "\n",
        "data['RATING'] = data['RATING'].apply(lambda x:rating(x))\n",
        "\n",
        "data['combined'] = data['REVIEW_TEXT'] + ' ' + data['RATING'] + ' ' + data['VERIFIED_PURCHASE'] + ' ' + data['PRODUCT_CATEGORY']"
      ],
      "metadata": {
        "id": "uhmc33UxBjK6"
      },
      "id": "uhmc33UxBjK6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Train and test split \n",
        "from sklearn.model_selection import train_test_split\n",
        "train_dataset, test_dataset = train_test_split(data, test_size=0.3, shuffle=True, stratify=None, random_state=42)\n",
        "train_dataset = train_dataset.reset_index(drop=True)\n",
        "test_dataset = test_dataset.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "BzfLZxDGmew6"
      },
      "id": "BzfLZxDGmew6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUKC7YMum1Ji",
        "outputId": "33dcc62c-1ff2-48ab-88d6-b7f86e18db0d"
      },
      "id": "PUKC7YMum1Ji",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14700, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.LABEL.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tz8cdhL7JK7J",
        "outputId": "7ce8c99f-430a-4fa6-d2e2-cd8eb7d29e70"
      },
      "id": "tz8cdhL7JK7J",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7389\n",
              "1    7311\n",
              "Name: LABEL, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.LABEL.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocxxao_Ym5AI",
        "outputId": "b2d14ec1-4647-4b7d-f434-39c558a76cd6"
      },
      "id": "ocxxao_Ym5AI",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    3189\n",
              "0    3111\n",
              "Name: LABEL, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from transformers import AlbertTokenizer, TFAlbertModel, AlbertConfig, TFAutoModelForSequenceClassification\n",
        "from transformers import AutoTokenizer"
      ],
      "metadata": {
        "id": "QLdTQxaCRt59"
      },
      "id": "QLdTQxaCRt59",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "ePGqQKx_Sjvi"
      },
      "id": "ePGqQKx_Sjvi",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "JDsTRRFLPjxm",
        "outputId": "6b407e49-7d70-4dc4-a8d6-c8084f6c3bfb"
      },
      "id": "JDsTRRFLPjxm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   LABEL RATING VERIFIED_PURCHASE PRODUCT_CATEGORY  \\\n",
              "0      0   Good               YES  Lawn and Garden   \n",
              "1      0    Avg               YES               PC   \n",
              "\n",
              "                                       PRODUCT_TITLE  \\\n",
              "0  Grace Alley Nylon American Flag with Embroider...   \n",
              "1  ASUS VivoPC VM62N-G039R (Discontinued by Manuf...   \n",
              "\n",
              "                 REVIEW_TITLE  \\\n",
              "0  Good flag at descent price   \n",
              "1      1 is horrible as usual   \n",
              "\n",
              "                                         REVIEW_TEXT  sentiment_score  \\\n",
              "0  The flag arrived on time,, and appears to be g...         0.700000   \n",
              "1  Bought this to become my new media center PC. ...        -0.028864   \n",
              "\n",
              "  sentiment_label  review_length  \\\n",
              "0        Positive            107   \n",
              "1        Negative            357   \n",
              "\n",
              "                                            combined  \n",
              "0  The flag arrived on time,, and appears to be g...  \n",
              "1  Bought this to become my new media center PC. ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-020385a8-5981-4803-b814-f306889dbb85\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LABEL</th>\n",
              "      <th>RATING</th>\n",
              "      <th>VERIFIED_PURCHASE</th>\n",
              "      <th>PRODUCT_CATEGORY</th>\n",
              "      <th>PRODUCT_TITLE</th>\n",
              "      <th>REVIEW_TITLE</th>\n",
              "      <th>REVIEW_TEXT</th>\n",
              "      <th>sentiment_score</th>\n",
              "      <th>sentiment_label</th>\n",
              "      <th>review_length</th>\n",
              "      <th>combined</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Good</td>\n",
              "      <td>YES</td>\n",
              "      <td>Lawn and Garden</td>\n",
              "      <td>Grace Alley Nylon American Flag with Embroider...</td>\n",
              "      <td>Good flag at descent price</td>\n",
              "      <td>The flag arrived on time,, and appears to be g...</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>Positive</td>\n",
              "      <td>107</td>\n",
              "      <td>The flag arrived on time,, and appears to be g...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Avg</td>\n",
              "      <td>YES</td>\n",
              "      <td>PC</td>\n",
              "      <td>ASUS VivoPC VM62N-G039R (Discontinued by Manuf...</td>\n",
              "      <td>1 is horrible as usual</td>\n",
              "      <td>Bought this to become my new media center PC. ...</td>\n",
              "      <td>-0.028864</td>\n",
              "      <td>Negative</td>\n",
              "      <td>357</td>\n",
              "      <td>Bought this to become my new media center PC. ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-020385a8-5981-4803-b814-f306889dbb85')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-020385a8-5981-4803-b814-f306889dbb85 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-020385a8-5981-4803-b814-f306889dbb85');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset['combined'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "YJKEmrzNCzHC",
        "outputId": "4a5b40e4-5116-40c9-b214-d932dbc84f13"
      },
      "id": "YJKEmrzNCzHC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Bought this to become my new media center PC. Windows 8.1 is horrible as usual. Installed XBMC and VLC to try and play the videos I have. The memory in the original unit is just isn't enough. Spent another $100 of RAM and finally can play the videos from my NAS smoothly. The unit is pretty looking and passes the wife requirement to sit in the living room. Avg YES PC\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "train_dataset['LABEL'] = le.fit_transform(train_dataset['LABEL'])"
      ],
      "metadata": {
        "id": "iA9l9Aa92aSf"
      },
      "id": "iA9l9Aa92aSf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.LABEL.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zV41xx3FJu6c",
        "outputId": "bfbbd8db-50f2-4a43-c282-d84ef15b7e4c"
      },
      "id": "zV41xx3FJu6c",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7389\n",
              "1    7311\n",
              "Name: LABEL, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X1 = train_dataset['combined']\n",
        "y = train_dataset['LABEL']"
      ],
      "metadata": {
        "id": "QJR9eZFKRJOK"
      },
      "id": "QJR9eZFKRJOK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_val,y_train,y_val = train_test_split(X1,y, test_size=0.3, shuffle=True, stratify=None, random_state=42)"
      ],
      "metadata": {
        "id": "kUXrj_lkPY6x"
      },
      "id": "kUXrj_lkPY6x",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2E8oMia2P2aN",
        "outputId": "90a1d5a2-e4ef-41eb-cf58-155d0bf7627f"
      },
      "id": "2E8oMia2P2aN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10290,), (10290,))"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_val.shape, y_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6I32SxuPP2Wy",
        "outputId": "6594141d-25dd-4aaa-92b0-b9d59f7972c4"
      },
      "id": "6I32SxuPP2Wy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4410,), (4410,))"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install sentencepiece"
      ],
      "metadata": {
        "id": "VVWK9nn9S7B9"
      },
      "id": "VVWK9nn9S7B9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = \"albert-base-v2\"\n",
        "# albert-large-v2, albert-xlarge-v2, albert-xxlarge-v2\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
        "#tokenizer = transformers.AlbertTokenizer.from_pretrained(MODEL_NAME)"
      ],
      "metadata": {
        "id": "jzUMI5HpP2UU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "39e6543c4b794ae1981653b01a638440",
            "0898cf34cd6e426ebc39c432c2fd82c0",
            "f0fa8852a5a24397a8b1db27b738cae4",
            "09986d0f609742e58985c7584e05e7b3",
            "6ce9132cd4cc47e591769b963014ab9e",
            "920446b0300f4fe99c2bb0041fc8607c",
            "b20c3867e0ed4ae9889a184d7a08baad",
            "9d15b2707ed245b6bf5216bf1403a5ed",
            "293d4b232bb44bfcb25262723e26362e",
            "ec3ed9013fd24b26ac524b2a171dac8f",
            "f973dd477a6e4f90b7771ff5eeb5f66e",
            "972b657947994a5ca6af4d4f7b5b89a5",
            "3407ab1348464fe29c4a8f99982d2fa2",
            "b500251af6f6451ab46ac43f66db71f8",
            "e7c087392d194b8c917f94120a7c8587",
            "9977a4983d684879b07464a1e928322a",
            "4a1453b82af2420da8797c488f43b4c5",
            "cd28aa4a1f8d4d359c2788b8a993621a",
            "dd2692162d9a4507bf288e1286298955",
            "a785492c557a418399f8da10a6b76aab",
            "8ed4520169174ac5b2a0d4945900fdbb",
            "40e01a8452ab43c9a9aea2cb4cdb8b4e",
            "dd6000764fe54127bf169313a3a3ed24",
            "1210f86ef0624ea491f3ed9dbe395649",
            "be85585c5092449988bf37b6b3c337ca",
            "d4786ac4a9d6451da89bb7f2f27aa378",
            "f974bd9d4b47431ebc48715b7b7146f0",
            "69a5ece318a940dc91a38db026588448",
            "c2dc15200d0042c39bdc2673f6f9a1fd",
            "5da4893fe03142f3ae9bf6fb5f0ad6fb",
            "beb82fae059a45aeaec902cb69841b16",
            "bc5f553f6bff4457aa0cf1761b57bc59",
            "4e4a26dcc60947c7a891b735430b68d3"
          ]
        },
        "outputId": "7d8bae79-8d7d-415b-ecd7-e8a9a7e67a04"
      },
      "id": "jzUMI5HpP2UU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/684 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39e6543c4b794ae1981653b01a638440"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/742k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "972b657947994a5ca6af4d4f7b5b89a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.json:   0%|          | 0.00/1.25M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd6000764fe54127bf169313a3a3ed24"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_val = 0\n",
        "for sent in (X_train.tolist() + X_train.tolist() + X_val.tolist()):\n",
        "    try:\n",
        "        sent_tok_len = len(tokenizer.tokenize(sent))\n",
        "        max_val = sent_tok_len if (sent_tok_len > max_val) else max_val\n",
        "    except:\n",
        "        pass\n",
        "    \n",
        "print(f\"The maximum amount of tokens in the dataset is {max_val}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-f0XGbqP2SY",
        "outputId": "03e96a0f-bac3-48dd-fdab-c569ed7d526d"
      },
      "id": "2-f0XGbqP2SY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (646 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The maximum amount of tokens in the dataset is 3774\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MAX_LENGTH = 512\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME,  \n",
        "                                                add_special_tokens=True,\n",
        "                                                max_length=MAX_LENGTH, \n",
        "                                                pad_to_max_length=True)\n",
        "\n",
        "def tokenize(sentences, tokenizer):\n",
        "    input_ids, input_masks, input_segments = [], [], []\n",
        "    for sentence in tqdm(sentences):\n",
        "        inputs = tokenizer.encode_plus(sentence, \n",
        "                                       add_special_tokens=True, \n",
        "                                       max_length=MAX_LENGTH, \n",
        "                                       pad_to_max_length=True, \n",
        "                                       return_attention_mask=True, \n",
        "                                       return_token_type_ids=True, \n",
        "                                       truncation=True)\n",
        "        input_ids.append(inputs['input_ids'])\n",
        "        input_masks.append(inputs['attention_mask'])\n",
        "        input_segments.append(inputs['token_type_ids'])        \n",
        "        \n",
        "    return np.asarray(input_ids, dtype='int32'), np.asarray(input_masks, dtype='int32')"
      ],
      "metadata": {
        "id": "M9x9UOnNRx-o"
      },
      "id": "M9x9UOnNRx-o",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_token = tokenize(X_train, tokenizer)\n",
        "X_val_token = tokenize(X_val, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuJ6FPeURx7P",
        "outputId": "a80ba144-221d-4e04-e779-44133094c95a"
      },
      "id": "HuJ6FPeURx7P",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10290/10290 [00:04<00:00, 2480.00it/s]\n",
            "100%|██████████| 4410/4410 [00:01<00:00, 2671.89it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1jA6BN3aU4A",
        "outputId": "cf9e6d88-09b5-4ca3-dccb-856480e1af89"
      },
      "id": "N1jA6BN3aU4A",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[    2,    48,  7533, ...,     0,     0,     0],\n",
              "        [    2,    48,  2146, ...,     0,     0,     0],\n",
              "        [    2,   158,    50, ...,     0,     0,     0],\n",
              "        ...,\n",
              "        [    2,    51,  5606, ...,     0,     0,     0],\n",
              "        [    2, 18774,   279, ...,     0,     0,     0],\n",
              "        [    2,    32,    22, ...,     0,     0,     0]], dtype=int32),\n",
              " array([[1, 1, 1, ..., 0, 0, 0],\n",
              "        [1, 1, 1, ..., 0, 0, 0],\n",
              "        [1, 1, 1, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [1, 1, 1, ..., 0, 0, 0],\n",
              "        [1, 1, 1, ..., 0, 0, 0],\n",
              "        [1, 1, 1, ..., 0, 0, 0]], dtype=int32))"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index_num = 10\n",
        "\n",
        "print(train_dataset.loc[index_num]['LABEL'], '\\n')\n",
        "\n",
        "print(X_train[index_num], '\\n')\n",
        "\n",
        "temp_tokens = tokenizer.tokenize(X_train[index_num])\n",
        "temp_ids = tokenizer.encode(X_train[index_num])\n",
        "\n",
        "print('ID\\'s', 'Input Tokens', sep='\\t')\n",
        "for i in range(len(temp_ids)):\n",
        "    if i == 0:\n",
        "        print(temp_ids[i], '[CLS]', sep='\\t')\n",
        "        continue\n",
        "    if i == len(temp_ids)-1:\n",
        "        print(temp_ids[i], '[SEP]', sep='\\t')\n",
        "        break\n",
        "    print(temp_ids[i], temp_tokens[i-1], sep='\\t')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gGSWmOjRx4X",
        "outputId": "eceb368a-3fab-4096-b77d-b4e39b20bf00"
      },
      "id": "8gGSWmOjRx4X",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 \n",
            "\n",
            "These are pretty standard lights...they work...they haven't spontaneously exploded or anything. I purchased them to turn into flower lights (they haven't caught on fire either which is also a plus) and they've been affixed to my wall for the past two years. They still work, but I don't turn them on all that often. Very Good YES Lawn and Garden \n",
            "\n",
            "ID's\tInput Tokens\n",
            "2\t[CLS]\n",
            "158\t▁these\n",
            "50\t▁are\n",
            "1772\t▁pretty\n",
            "1236\t▁standard\n",
            "3411\t▁lights\n",
            "9\t.\n",
            "9\t.\n",
            "9\t.\n",
            "2077\tthey\n",
            "170\t▁work\n",
            "9\t.\n",
            "9\t.\n",
            "9\t.\n",
            "2077\tthey\n",
            "2933\t▁haven\n",
            "22\t'\n",
            "38\tt\n",
            "27602\t▁spontaneously\n",
            "10426\t▁exploded\n",
            "54\t▁or\n",
            "602\t▁anything\n",
            "9\t.\n",
            "31\t▁i\n",
            "2432\t▁purchased\n",
            "105\t▁them\n",
            "20\t▁to\n",
            "805\t▁turn\n",
            "77\t▁into\n",
            "5383\t▁flower\n",
            "3411\t▁lights\n",
            "13\t▁\n",
            "5\t(\n",
            "2077\tthey\n",
            "2933\t▁haven\n",
            "22\t'\n",
            "38\tt\n",
            "1383\t▁caught\n",
            "27\t▁on\n",
            "535\t▁fire\n",
            "694\t▁either\n",
            "56\t▁which\n",
            "25\t▁is\n",
            "67\t▁also\n",
            "21\t▁a\n",
            "3123\t▁plus\n",
            "6\t)\n",
            "17\t▁and\n",
            "59\t▁they\n",
            "22\t'\n",
            "195\tve\n",
            "74\t▁been\n",
            "26568\t▁affix\n",
            "69\ted\n",
            "20\t▁to\n",
            "51\t▁my\n",
            "769\t▁wall\n",
            "26\t▁for\n",
            "14\t▁the\n",
            "640\t▁past\n",
            "81\t▁two\n",
            "122\t▁years\n",
            "9\t.\n",
            "59\t▁they\n",
            "174\t▁still\n",
            "170\t▁work\n",
            "15\t,\n",
            "47\t▁but\n",
            "31\t▁i\n",
            "221\t▁don\n",
            "22\t'\n",
            "38\tt\n",
            "805\t▁turn\n",
            "105\t▁them\n",
            "27\t▁on\n",
            "65\t▁all\n",
            "30\t▁that\n",
            "478\t▁often\n",
            "9\t.\n",
            "253\t▁very\n",
            "254\t▁good\n",
            "1643\t▁yes\n",
            "9413\t▁lawn\n",
            "17\t▁and\n",
            "1986\t▁garden\n",
            "3\t[SEP]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Model creation\n",
        "\n",
        "config = AlbertConfig.from_pretrained(MODEL_NAME, output_hidden_states=True, output_attentions=True)\n",
        "ALBERT =  TFAlbertModel.from_pretrained(MODEL_NAME, config=config)\n",
        "#TFAutoModelForSequenceClassification\n",
        "\n",
        "input_ids_in = tf.keras.layers.Input(shape=(MAX_LENGTH,), name='input_token', dtype='int32')\n",
        "input_masks_in = tf.keras.layers.Input(shape=(MAX_LENGTH,), name='masked_token', dtype='int32') \n",
        "\n",
        "embedding_layer = ALBERT(input_ids = input_ids_in, attention_mask = input_masks_in)[0]\n",
        "X = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(512, return_sequences=True))(embedding_layer)\n",
        "X = tf.keras.layers.GlobalMaxPool1D()(X)\n",
        "X = tf.keras.layers.Dense(128, activation='relu')(X)\n",
        "X = tf.keras.layers.Dropout(0.1)(X)\n",
        "X = tf.keras.layers.Dense(2, activation='sigmoid')(X)\n",
        "\n",
        "model = tf.keras.Model(inputs=[input_ids_in, input_masks_in], outputs = X)\n",
        "\n",
        "for layer in model.layers[:3]:\n",
        "    layer.trainable = False\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "8135be93a41b42758d16ae34de760437",
            "27553babbbf44b738f5323cf5de86938",
            "e72bf6b9ca6e4452a2a3e9b06a497ad3",
            "749a568a1629426a8725634bc134517e",
            "3a7e6e8def184a3f818af53a2916b8b6",
            "4f12ff9bb20747ecbc9bb5ebde73593d",
            "9342d340648b49ea9a8b79683a3b4d7b",
            "a4f6cc02b3a6485cabc2dc2a9c88f78b",
            "1f457b38f20b403194e54daeae267fe1",
            "c7c0efd1d2e74c40a5a81756301848fa",
            "e4e42f4eeba1420a93b6e8dd9cb9a116"
          ]
        },
        "id": "xUovfsXzRx2F",
        "outputId": "84aaedc6-620a-495f-ab72-624f6a98e14e"
      },
      "id": "xUovfsXzRx2F",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/60.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8135be93a41b42758d16ae34de760437"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at albert-base-v2 were not used when initializing TFAlbertModel: ['predictions']\n",
            "- This IS expected if you are initializing TFAlbertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFAlbertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFAlbertModel were initialized from the model checkpoint at albert-base-v2.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFAlbertModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_token (InputLayer)       [(None, 512)]        0           []                               \n",
            "                                                                                                  \n",
            " masked_token (InputLayer)      [(None, 512)]        0           []                               \n",
            "                                                                                                  \n",
            " tf_albert_model (TFAlbertModel  TFBaseModelOutputWi  11683584   ['input_token[0][0]',            \n",
            " )                              thPooling(last_hidd               'masked_token[0][0]']           \n",
            "                                en_state=(None, 512                                               \n",
            "                                , 768),                                                           \n",
            "                                 pooler_output=(Non                                               \n",
            "                                e, 768),                                                          \n",
            "                                 hidden_states=((No                                               \n",
            "                                ne, 512, 768),                                                    \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768),                                                \n",
            "                                 (None, 512, 768)),                                               \n",
            "                                 attentions=((None,                                               \n",
            "                                 12, None, 512),                                                  \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12),                                                              \n",
            "                                 (None, 12, None, 5                                               \n",
            "                                12)))                                                             \n",
            "                                                                                                  \n",
            " bidirectional (Bidirectional)  (None, 512, 1024)    5246976     ['tf_albert_model[0][25]']       \n",
            "                                                                                                  \n",
            " global_max_pooling1d (GlobalMa  (None, 1024)        0           ['bidirectional[0][0]']          \n",
            " xPooling1D)                                                                                      \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 128)          131200      ['global_max_pooling1d[0][0]']   \n",
            "                                                                                                  \n",
            " dropout_4 (Dropout)            (None, 128)          0           ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 2)            258         ['dropout_4[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 17,062,018\n",
            "Trainable params: 5,378,434\n",
            "Non-trainable params: 11,683,584\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Create an output directory\n",
        "import os\n",
        "output_dir = './model1_outputs_albert'\n",
        "if not os.path.exists(output_dir): ### If the file directory doesn't already exists,\n",
        "    os.makedirs(output_dir) ### Make it please"
      ],
      "metadata": {
        "id": "AfvuH8XJTlsU"
      },
      "id": "AfvuH8XJTlsU",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
        "model_checkpoint = ModelCheckpoint(filepath=output_dir+'/weights.{epoch:02d}.hdf5',\n",
        "                                  save_weights_only=True)\n",
        "\n",
        "early_stopping = EarlyStopping(patience=3, # Stop after 3 epochs of no improvement\n",
        "                               monitor='val_loss', # Look at validation_loss\n",
        "                               min_delta=0, # After 0 change\n",
        "                               mode='min', # Stop when quantity has stopped decreasing\n",
        "                               restore_best_weights=False, # Don't Restore the best weights\n",
        "                               verbose=1) \n",
        "\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', # Look at validation loss\n",
        "                              min_lr=0.00001, # Lower bound of learning rate\n",
        "                              patience=1, # Reduce after 1 with little change\n",
        "                              mode='min', # Stop when quantity has stopped decreasing\n",
        "                              factor=0.1, # Reduce by a factor of 1/10\n",
        "                              min_delta=0.01, # Minimumn change needed\n",
        "                              verbose=1)"
      ],
      "metadata": {
        "id": "6saBBa0xTlp8"
      },
      "id": "6saBBa0xTlp8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(X_train_token.shape)\n",
        "print(y_train.shape)\n",
        "#print(X_test_token.shape)\n",
        "print(y_val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cYIQBl_UlOT",
        "outputId": "578f6995-8afc-43a5-9cfb-05df3e36a087"
      },
      "id": "9cYIQBl_UlOT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10290,)\n",
            "(4410,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mC32qS5P0CVi",
        "outputId": "ea14e568-fc78-4b0d-def2-48e7ff5f34af"
      },
      "id": "mC32qS5P0CVi",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "567      0\n",
              "7053     1\n",
              "4785     1\n",
              "2245     0\n",
              "4165     0\n",
              "        ..\n",
              "5191     1\n",
              "13418    1\n",
              "5390     1\n",
              "860      1\n",
              "7270     1\n",
              "Name: LABEL, Length: 10290, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "y_train = to_categorical(y_train)\n",
        "y_val = to_categorical(y_val)"
      ],
      "metadata": {
        "id": "vLM_VGP8zwWJ"
      },
      "id": "vLM_VGP8zwWJ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rw28mCcz0Igd",
        "outputId": "56b6fcbf-bfbe-4fc5-e0b3-2a7dc1b2a566"
      },
      "id": "Rw28mCcz0Igd",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0.],\n",
              "       [0., 1.],\n",
              "       [0., 1.],\n",
              "       ...,\n",
              "       [0., 1.],\n",
              "       [0., 1.],\n",
              "       [0., 1.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer= 'Adam', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train_token, \n",
        "                    y_train, \n",
        "                    epochs = 10,\n",
        "                    batch_size=32, \n",
        "                    validation_data=(X_val_token, y_val), \n",
        "                    callbacks=[model_checkpoint, early_stopping, reduce_lr])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KjQaC4odTlnW",
        "outputId": "06f41ce2-d892-40f1-ad25-677ac2ed4f8d"
      },
      "id": "KjQaC4odTlnW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "322/322 [==============================] - 443s 1s/step - loss: 0.5375 - accuracy: 0.7401 - val_loss: 0.4314 - val_accuracy: 0.8145 - lr: 0.0010\n",
            "Epoch 2/10\n",
            "322/322 [==============================] - 424s 1s/step - loss: 0.4253 - accuracy: 0.8205 - val_loss: 0.4130 - val_accuracy: 0.8236 - lr: 0.0010\n",
            "Epoch 3/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.3836 - accuracy: 0.8385\n",
            "Epoch 3: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "322/322 [==============================] - 424s 1s/step - loss: 0.3836 - accuracy: 0.8385 - val_loss: 0.4161 - val_accuracy: 0.8265 - lr: 0.0010\n",
            "Epoch 4/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.3037 - accuracy: 0.8828\n",
            "Epoch 4: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "322/322 [==============================] - 424s 1s/step - loss: 0.3037 - accuracy: 0.8828 - val_loss: 0.4043 - val_accuracy: 0.8336 - lr: 1.0000e-04\n",
            "Epoch 5/10\n",
            "322/322 [==============================] - 424s 1s/step - loss: 0.2612 - accuracy: 0.9019 - val_loss: 0.4003 - val_accuracy: 0.8365 - lr: 1.0000e-05\n",
            "Epoch 6/10\n",
            "322/322 [==============================] - ETA: 0s - loss: 0.2565 - accuracy: 0.9037\n",
            "Epoch 6: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
            "322/322 [==============================] - 424s 1s/step - loss: 0.2565 - accuracy: 0.9037 - val_loss: 0.4006 - val_accuracy: 0.8367 - lr: 1.0000e-05\n",
            "Epoch 7/10\n",
            "322/322 [==============================] - 424s 1s/step - loss: 0.2520 - accuracy: 0.9057 - val_loss: 0.4010 - val_accuracy: 0.8372 - lr: 1.0000e-05\n",
            "Epoch 8/10\n",
            "322/322 [==============================] - 424s 1s/step - loss: 0.2460 - accuracy: 0.9090 - val_loss: 0.4023 - val_accuracy: 0.8333 - lr: 1.0000e-05\n",
            "Epoch 8: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plotting the training Accuracy and Loss\n",
        "def plot_history(history):\n",
        "    acc = history.history['accuracy']\n",
        "    val_acc = history.history['val_accuracy']\n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "    x = range(1, len(acc) + 1)\n",
        "\n",
        "    plt.figure(figsize=(12, 5))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(x, acc, 'b', label='Training acc')\n",
        "    plt.plot(x, val_acc, 'r', label='Validation acc')\n",
        "    plt.title('Training and validation accuracy')\n",
        "    plt.legend()\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(x, loss, 'b', label='Training loss')\n",
        "    plt.plot(x, val_loss, 'r', label='Validation loss')\n",
        "    plt.title('Training and validation loss')\n",
        "    plt.legend()\n",
        "    print(\"Lowest Validation Loss: epoch {}\".format(np.argmin(val_loss)+1))\n",
        "    print(\"Highest Validation Accuracy: epoch {}\".format(np.argmax(val_acc)+1))\n",
        "\n",
        "plot_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "ANm2BDCiTlkk",
        "outputId": "edf2675e-033f-44fe-eb2f-039167a88c7e"
      },
      "id": "ANm2BDCiTlkk",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lowest Validation Loss: epoch 5\n",
            "Highest Validation Accuracy: epoch 7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_max_val_acc_epoch(history):\n",
        "    return \"0\"+str(np.argmax(history.history['val_accuracy'])+1)"
      ],
      "metadata": {
        "id": "yZQRmys0uxrr"
      },
      "id": "yZQRmys0uxrr",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epoch_num = get_max_val_acc_epoch(history)\n",
        "model.load_weights(output_dir+\"/weights.\"+epoch_num+\".hdf5\") # Load in model weights"
      ],
      "metadata": {
        "id": "rZe9hqfRTliG"
      },
      "id": "rZe9hqfRTliG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aPWvpMnx3zFZ",
        "outputId": "f58982a2-5872-42b2-cbe0-b7560ab53b2f"
      },
      "id": "aPWvpMnx3zFZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6300, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = test_dataset['combined']"
      ],
      "metadata": {
        "id": "gm_cfVmYu4aW"
      },
      "id": "gm_cfVmYu4aW",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset['LABEL'] = le.fit_transform(test_dataset['LABEL'])"
      ],
      "metadata": {
        "id": "g9RCstTcviCo"
      },
      "id": "g9RCstTcviCo",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = test_dataset['LABEL']"
      ],
      "metadata": {
        "id": "YcqYGFvgv0kB"
      },
      "id": "YcqYGFvgv0kB",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qpK5VpYVwg15",
        "outputId": "e313dd44-fd40-4239-ad9e-c772837e0000"
      },
      "id": "qpK5VpYVwg15",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    3189\n",
              "0    3111\n",
              "Name: LABEL, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = to_categorical(np.asarray(y_test))"
      ],
      "metadata": {
        "id": "NmuOy10Ez7IN"
      },
      "id": "NmuOy10Ez7IN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_token = tokenize(X_test, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Om6Kg2ngvGXN",
        "outputId": "b97c7f6a-372c-48ed-8298-40d0ca8774ca"
      },
      "id": "Om6Kg2ngvGXN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6300/6300 [00:02<00:00, 2164.81it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(X_test_token)"
      ],
      "metadata": {
        "id": "FL3mnMqrTsF-"
      },
      "id": "FL3mnMqrTsF-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"ROC-AUC Score\", roc_auc_score(y_test,np.round(abs(pred))))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, np.round(abs(pred)), target_names = [\"real\", \"fake\"]))\n",
        "print('Confusion matrix:\\n',confusion_matrix(y_true = y_test.argmax(axis=1), y_pred = pred.argmax(axis=1)))\n",
        "print(\"ALBERT Accuracy: \",accuracy_score(y_test,  np.round(abs(pred))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6UOpo1JTsCd",
        "outputId": "30f215d1-b2f2-472d-e7a6-eb03e32b165f"
      },
      "id": "U6UOpo1JTsCd",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC-AUC Score 0.8317761785404444\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "        real       0.81      0.86      0.83      3111\n",
            "        fake       0.86      0.80      0.83      3189\n",
            "\n",
            "   micro avg       0.83      0.83      0.83      6300\n",
            "   macro avg       0.83      0.83      0.83      6300\n",
            "weighted avg       0.83      0.83      0.83      6300\n",
            " samples avg       0.83      0.83      0.83      6300\n",
            "\n",
            "Confusion matrix:\n",
            " [[2676  435]\n",
            " [ 628 2561]]\n",
            "ALBERT Accuracy:  0.8276190476190476\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hnXdaJ_2hVX6"
      },
      "id": "hnXdaJ_2hVX6",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "ALBERT Experiments- Fake review detection.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "c9b43f3f",
        "fc234d9c"
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      "machine_shape": "hm"
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