{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'custrev.*'\n",
    "text, label_text, label = [], [], []\n",
    "for file in glob.glob(path):\n",
    "    with open(file, 'r') as f:\n",
    "        cr, sentiment = file.split('.')\n",
    "        \n",
    "        if sentiment == 'pos':\n",
    "            lab_text = 'positive'\n",
    "            lab = 1\n",
    "\n",
    "        else:\n",
    "            lab_text = 'negative'\n",
    "            lab = 0\n",
    "\n",
    "        \n",
    "        for line in f:\n",
    "            if line.strip():\n",
    "                text.append(line.rstrip())\n",
    "                label_text.append(lab_text)\n",
    "                label.append(lab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'text':text, 'label':label, 'label_text':label_text}).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = df.sample(frac=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df.drop(test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('test.jsonl', 'w') as f:\n",
    "    f.write(test.to_json(orient='records', lines=True))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('train.jsonl', 'w') as f:\n",
    "    f.write(train.to_json(orient='records', lines=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "vscode": {
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