Create_Balanced_Dataset.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/misha345a/E-commerce_Reviews_Classifier/blob/main/Balance_Datasets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Goal\n",
        "This script is used for cleaning the original Kaggle dataset, as well as preparing both **upsampled** and **text augmented** datasets for model training. <br> These steps are performed seperately because of their lengthy runtimes (i.e. augmentation requires 5hrs+).\n",
        "<br><br>\n",
        "Women's E-Commerce Clothing Reviews dataset from Kaggle:\n",
        "<br>\n",
        "https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews"
      ],
      "metadata": {
        "id": "a6EgaGuTa7yY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w9eJUaxBa1bb"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "\n",
        "!pip install opendatasets\n",
        "!pip install contractions\n",
        "!pip install autocorrect\n",
        "!pip install openpyxl -U"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# library imports\n",
        "import pandas as pd\n",
        "import opendatasets\n",
        "import contractions\n",
        "from autocorrect import Speller\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from tqdm.notebook import tqdm_notebook\n",
        "import nltk\n",
        "import re\n",
        "\n",
        "# initiate tqdm for pandas.apply() functions\n",
        "tqdm_notebook.pandas()"
      ],
      "metadata": {
        "id": "rXTD4wa4a35E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# expand notebook display options for dataframes\n",
        "pd.set_option('display.max_colwidth', 200)\n",
        "pd.options.display.max_columns = 999\n",
        "pd.options.display.max_rows = 300"
      ],
      "metadata": {
        "id": "VYJ-cNjfa38C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Kaggle Dataset"
      ],
      "metadata": {
        "id": "fP-HnH29fRw2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# option to directly retrieve the dataset from Kaggle\n",
        "kaggle_url = 'https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews'\n",
        "opendatasets.download(kaggle_url)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0KmZOqKa3_i",
        "outputId": "1f4da594-6398-4f49-d537-5b3b494dd0fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: misha345\n",
            "Your Kaggle Key: ··········\n",
            "Downloading womens-ecommerce-clothing-reviews.zip to ./womens-ecommerce-clothing-reviews\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2.79M/2.79M [00:00<00:00, 109MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the dataset \n",
        "raw_data = pd.read_csv('/content/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv')"
      ],
      "metadata": {
        "id": "7G68DN8fe0y7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# remove irrelevant columns\n",
        "dataset = raw_data[['Review Text', 'Recommended IND']]\n",
        "\n",
        "# remove observations with missing reviews\n",
        "dataset = dataset[~dataset['Review Text'].isna()]"
      ],
      "metadata": {
        "id": "AyGzEyTde01t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis of Target Classes "
      ],
      "metadata": {
        "id": "gYsCCfpxf4xQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define observation counts within each class\n",
        "class_counts = dataset['Recommended IND'].value_counts()"
      ],
      "metadata": {
        "id": "fCaOz7MWe03Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# display a bar chart of the distribution\n",
        "plt.figure(figsize = (7, 5))\n",
        "\n",
        "# define colors\n",
        "colors = ['#528CAD' if (x < max(class_counts)) else '#FF914D' for x in class_counts]\n",
        "\n",
        "ax = sns.countplot(x=dataset['Recommended IND'], \n",
        "                   data=dataset,\n",
        "                   palette=colors)\n",
        "ax.set(title='Imbalanced Class Distribution')\n",
        "\n",
        "# place labels above each bar\n",
        "vals = class_counts.to_list()\n",
        "vals.sort()\n",
        "ax_labels = vals \n",
        "rects = ax.patches\n",
        "labels = [ax_labels[i] for i in range(len(rects))]\n",
        "\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    ax.text(\n",
        "        rect.get_x() + rect.get_width() / 2, height + 5, label, ha=\"center\", va=\"bottom\"\n",
        "    )\n",
        "\n",
        "# edit x-tick labels\n",
        "ax.set_xticklabels(['0 (Not Recommended)', \"1 (Recommended)\"])\n",
        "\n",
        "# y-axis label\n",
        "ax.set_ylabel(\"Counts\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "Xfc7Hyxqe05q",
        "outputId": "ba37fe30-aecc-4c00-8946-a92f12c0e3f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the chart figure\n",
        "fig = ax.get_figure()\n",
        "fig.savefig('Class_Imbalance_BarChart.png', \n",
        "            dpi=75, \n",
        "            bbox_inches=\"tight\")"
      ],
      "metadata": {
        "id": "QzQPGwPde08A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing\n",
        "- Lowercase text\n",
        "- Fix common slang and abbreviations\n",
        "- Expanding contractions\n",
        "- Autocorrect misspelled words"
      ],
      "metadata": {
        "id": "QeJhH4_m6sIR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make all characters uniformly lowercase\n",
        "dataset['Review Text'] = dataset['Review Text'].apply(lambda x: x.lower())"
      ],
      "metadata": {
        "id": "MRbCsEaie1BI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_slang(text): \n",
        "    \"\"\"\n",
        "    Correct common slang and abbreviations.\n",
        "    \"\"\"\n",
        "    text = re.sub(r\"\\babt?\\b\", \"about\", text)\n",
        "    text = re.sub(r\"\\bcomfy\\b\", \"comfortable\", text)\n",
        "    text = re.sub(r\"\\brll?y\\b\", \"really\", text)\n",
        "    text = re.sub(r\"\\bso{2,}\\b\", \"so\", text)\n",
        "    text = re.sub(r\"\\bmed\\b\", \"medium\", text)\n",
        "    text = re.sub(r\"\\bxx?s\\b\", \"extra small\", text)\n",
        "    text = re.sub(r\"\\bxx?l\\b\", \"extra large\", text)\n",
        "    text = re.sub(r\"\\bfab\\b\", \"fabulous\", text)\n",
        "    text = re.sub(r\"\\bblk\\b\", \"black\", text)\n",
        "    text = re.sub(r\"\\bpromo\\b\", \"promotion\", text)\n",
        "    text = re.sub(r\"\\btts\\b\", \"true to size\", text)\n",
        "    text = re.sub(r\"\\blbs?\\b\", \"pounds\", text)\n",
        "    text = re.sub(r\"\\brn\\b\", \"right now\", text)\n",
        "    text = re.sub(r\"\\bwanna\\b\", \"want to\", text)\n",
        "    text = re.sub(r\"\\besp\\b\", \"especially\", text)\n",
        "    text = re.sub(r\"\\bgonn[ao]\\b\", \"going to\", text)\n",
        "    text = re.sub(r\"\\btho\\b\", \"though\", text)\n",
        "    text = re.sub(r\"altho \", \"although \", text)\n",
        "    text = re.sub(r\"prolly\", \"probably\", text)\n",
        "    text = re.sub(r\"asap\", \"as soon as possible\", text)\n",
        "    text = re.sub(r\"\\bbc|b/c\\b\", \"because\", text)\n",
        "    text = re.sub(r\"\\bavail\\b\", \"available\", text)\n",
        "    text = re.sub(r\"\\bdiff\\b\", \"different\", text)\n",
        "    text = re.sub(r\"\\bnxt|enxt\\b\", \"next\", text)\n",
        "    text = re.sub(r\" w/ \", \" with \", text)\n",
        "    text = re.sub(r\"\\bdidn \", \"didn't \", text)\n",
        "    text = re.sub(r\" dnt \", \" don't \", text)\n",
        "    text = re.sub(r\"\\bsnd\\b\", \"send\", text)\n",
        "    text = re.sub(r\"b4\\b\", \"before\", text)\n",
        "    text = re.sub(r\"stink'n\", \"stinking\", text)\n",
        "    text = re.sub(r\"\\batm\\b\", \"at the moment\", text)\n",
        "    text = re.sub(r\"\\besp\\b\", \"especially\", text)\n",
        "    text = re.sub(r\"\\bavg\\b\", \"average\", text)\n",
        "    text = re.sub(r\"(\\d'|\\d\\\")\\s?(\\d+'|\\d+\\\")?\", \" \", text) # removes height measurements\n",
        "    text = re.sub(r\"'\\d+\\w+'\", \" \", text) \n",
        "    text = re.sub(r\"\\s{2,}\", \" \", text).strip() \n",
        "\n",
        "    return text"
      ],
      "metadata": {
        "id": "6yiLbRA0e1C-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Review Text'] = dataset['Review Text'].progress_apply(clean_slang)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "3b687cb766df47dcb899908d1e423674",
            "9b141c00cd174af4a92cd5bb5d2e8cb9",
            "bf863f8a7e2e466187422fe61baeb415",
            "4233068fb9364ac99e6680e4edc5255e",
            "f4cea608b6ff47c1b51d07cb202fbfc6",
            "c6107e7495254b4bb7aeb4b0c580cd97",
            "25d8127dbc5d45dfa8dcaed40a8babb6",
            "de3999d1644e4d2e9ec26f71074b6fb8"
          ]
        },
        "id": "femL7KfbLSR9",
        "outputId": "b09d4d1b-67a2-4b6c-e8cf-64a2a3ba82ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3b687cb766df47dcb899908d1e423674",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=22641.0), HTML(value='')))"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cont_expand(text):\n",
        "    \"\"\"\n",
        "    Expand contractions (i.e don't->do not, can't->cannot).\n",
        "    \"\"\"\n",
        "    expanded_text = contractions.fix(text, slang=False)\n",
        "    return expanded_text\n",
        "\n",
        "# expand contractions\n",
        "dataset['Review Text'] = dataset['Review Text'].progress_apply(cont_expand)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "bf6dfe28a78142679ea92818ac0d5174",
            "b19b3acc1d92473da8c5f8011d484ce3",
            "534ca32cd25b415ba82d3c6dcf4c53a4",
            "fb797a10326c42eebc5d157b41f004dc",
            "35664d742e29456581a88087addbd836",
            "1c2ceb3bf4a24212a576c5a2de38132a",
            "59b90c6d90954c7ab7004b5668df1ab2",
            "3c21866a4dea4550bec41659f6898de1"
          ]
        },
        "id": "ypR_iwTEe1Hr",
        "outputId": "b6486f2b-d3a0-46f5-e736-5d87f6857c38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf6dfe28a78142679ea92818ac0d5174",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=22641.0), HTML(value='')))"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# autocorrect misspelled words (~25 minute runtime)\n",
        "spell_check = Speller(lang='en')\n",
        "dataset['Review Text'] = dataset['Review Text'].progress_apply(lambda x: spell_check(str(x)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "810d5da2cbb044eda3854791ac500604",
            "e5d641cd8cd544e1b886bd5ef6799f3d",
            "b8f74b44402449a1a81db8c8de8b6836",
            "713b8f40625644cd9e18ac6def9a6818",
            "0280dde8bcd2471b88e1f829e1cdcf2e",
            "72f45c2593b74e6aace7e4b2e9d02eb4",
            "7d03c5adac9845e79caa8a4917078ba9",
            "94fea21af4df4b04ab3863b1e562721b"
          ]
        },
        "id": "ikks481Ue1Kw",
        "outputId": "370806d6-2821-4191-c7d4-0674b3e13da8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "810d5da2cbb044eda3854791ac500604",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=22641.0), HTML(value='')))"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# export to Excel\n",
        "dataset.to_excel(\"Raw_Dataset_(Cleaned).xlsx\", \n",
        "                 header=True, \n",
        "                 index=False)"
      ],
      "metadata": {
        "id": "7F-i4u1zDwHa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.sample(5)"
      ],
      "metadata": {
        "id": "jGTL5VFLEMLt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Upsampling (Sklearn)"
      ],
      "metadata": {
        "id": "JBmR4cZIMIPX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import resample\n",
        "\n",
        "# upsample the minority class ('not-recommended') by duplicating random samples\n",
        "# 1 = recommended\n",
        "# 0 = not-recommended\n",
        "upsample = resample(dataset[dataset['Recommended IND']==0],\n",
        "                    replace=True, # sample with replacement\n",
        "                    n_samples=16104, # to match majority class\n",
        "                    random_state=42) # set reproducible results\n",
        "print(f\"Count of 'Not Recommended' examples (after upsampling): {len(upsample)}\")\n",
        "\n",
        "# combine the upsampled minority class with the rest of the data\n",
        "dataset = pd.concat([dataset[dataset['Recommended IND']!=0], upsample])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SecnuuyMMIhL",
        "outputId": "73f26b9d-93a9-4287-e37b-dad8ee22f4f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count of 'Not Recommended' examples (after upsampling): 16104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# export to Excel\n",
        "dataset.to_excel(\"Upsampled_Dataset.xlsx\", \n",
        "                 header=True, \n",
        "                 index=False)"
      ],
      "metadata": {
        "id": "y211wpVyMlpd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text Augmentation\n",
        "\n",
        "1 = Recommended; 18540 values (82%) <br>\n",
        "0 = Not Recommended; 4101 values (18%) <br>\n",
        "\n",
        "<br>\n",
        "There are significantly more \"Recommended\" observations than \"Not-Recommended\". Text augmentation is performed to increase the number of minority class examples. Ideally, this will lessen the model's bias towards majority class predictions (\"Recommended\") during training. Words are randomly swapped, deleted, and substituted with synonyms using a pre-trained word embedding model.  \n",
        "<br>\n",
        "\n",
        "<hr>\n",
        "\n",
        "### Example: \n",
        "\n",
        "<u>Original</u>:\n",
        "<br>\n",
        "This shirt is very flattering to all due to the adjustable front tie. \n",
        "it is the perfect length to wear with leggings and it is sleeveless so it pairs well with any cardigan. love this shirt!!!\n",
        "\n",
        "<u>Augmented Text:</u>\n",
        "<br>\n",
        "<b>this design</b> is very flattering <b>to most eyes towards</b> the adjustable <b>rear clasp</b>. \n",
        "it was the perfect <b>blouse</b> to wear with leggings <b>because</b> it is sleeveless so it <b>fit</b> well <b>against the dress</b>.\n",
        "love this <b>stuff</b>!!!\n"
      ],
      "metadata": {
        "id": "4ZFzgntgcz9w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "\n",
        "!pip install textattack\n",
        "!pip install tensorflow_text"
      ],
      "metadata": {
        "id": "0QMV0_5lc4BX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "from tqdm.notebook import tqdm_notebook\n",
        "import nltk\n",
        "import re\n",
        "\n",
        "# initiate tqdm for pandas.apply() functions\n",
        "tqdm_notebook.pandas()"
      ],
      "metadata": {
        "id": "EwiG1nALjY2x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load the dataset \n",
        "dataset = pd.read_excel('/content/Raw_Dataset_(Cleaned).xlsx')"
      ],
      "metadata": {
        "id": "8-x7kVKJc4ET"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define a dataframe of only negative reviews\n",
        "df_not_recommend = dataset[dataset['Recommended IND']==0]"
      ],
      "metadata": {
        "id": "e5FaBbOtc4Gg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize the augmentation model\n",
        "from textattack.augmentation import EasyDataAugmenter\n",
        "aug = EasyDataAugmenter(pct_words_to_swap=0.25, \n",
        "                        transformations_per_example=3)"
      ],
      "metadata": {
        "id": "6rLyvhQkc4JV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce800d90-8d67-4380-b10b-c5503b179179"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "textattack: Updating TextAttack package dependencies.\n",
            "textattack: Downloading NLTK required packages.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package omw to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/omw.zip.\n",
            "[nltk_data] Downloading package universal_tagset to /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/universal_tagset.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "textattack: Downloading https://textattack.s3.amazonaws.com/word_embeddings/paragramcf.\n",
            "100%|██████████| 481M/481M [00:49<00:00, 9.79MB/s]\n",
            "textattack: Unzipping file /root/.cache/textattack/tmpqy4slwth.zip to /root/.cache/textattack/word_embeddings/paragramcf.\n",
            "textattack: Successfully saved word_embeddings/paragramcf to cache.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aug_corpus = []\n",
        "\n",
        "def augmented_corpus(text):\n",
        "  \"\"\"\n",
        "  Augment text and append to an array.\n",
        "  \"\"\"\n",
        "  try:\n",
        "    aug_corpus.extend(aug.augment(text))\n",
        "  except:\n",
        "    pass"
      ],
      "metadata": {
        "id": "s-wfqfgIc4P_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_not_recommend['Review Text'].progress_apply(augmented_corpus)"
      ],
      "metadata": {
        "id": "ZRAwV-c5EMOn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_aug_result = pd.DataFrame(zip(aug_corpus, [0]*len(aug_corpus)), \n",
        "                             columns=['Review Text', 'Recommended IND'])\n",
        "\n",
        "# append augmented results to original 'Not Recommended' examples\n",
        "df_aug_result = df_not_recommend.append(df_aug_result)"
      ],
      "metadata": {
        "id": "qegS6A15mj4O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# export to Excel\n",
        "df_aug_result.to_excel('Augmented_Dataset.xlsx', \n",
        "                        header=True, \n",
        "                        index=False)"
      ],
      "metadata": {
        "id": "P-hSNyfiTVgu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df_aug_result))"
      ],
      "metadata": {
        "id": "zCs9NclDQn4Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text Augmentation Example"
      ],
      "metadata": {
        "id": "PxlTXXWP158X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aug.augment('these pants are beautiful but very sheer and very delicate. \\\n",
        "             not ideal for a mom with kids')"
      ],
      "metadata": {
        "id": "mPfx8CR_YqAT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "E76ermYV173o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}