BP2024/preprocessing/clustered_processing.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook does preprocessing, and pre-analysing data before the annotation\n",
    "The actions done in this notebook are \n",
    "- data merging\n",
    "- data analysis and visualisation\n",
    "- language identification\n",
    "- sentiment analysis\n",
    "- GDPR protection\n",
    "- samples containing swear words detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/A200119424/anaconda3/envs/sentiment/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "from transformers import pipeline\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import re\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# macros\n",
    "\n",
    "OPTIMUM_WIDTH = 0.6\n",
    "ALIGN = 'center'\n",
    "\n",
    "# colors\n",
    "BLUE = '#4040a1'\n",
    "GREEN = '#618685'\n",
    "YELLOW = '#feb236'\n",
    "ORANGE = '#f18973'\n",
    "AQUA = '#d5f4e6'\n",
    "BLACK = '#000000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_jsonl(file_path):\n",
    "    with open(file_path, 'r', encoding='utf-8') as file:\n",
    "        return [json.loads(line) for line in file]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_json(filename):\n",
    "    with open(filename) as json_file:\n",
    "        return json.load(json_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge ficos data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_fico = []\n",
    "for ficos_chunk in range(0,4):\n",
    "    merged_fico += load_jsonl(f'clustered_jsonl/fico_chunk_{ficos_chunk}.jsonl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_jsonl(filename, new_dataset):\n",
    "    with open(f'{filename}l', 'w') as jsonl_file:\n",
    "        for item in new_dataset:\n",
    "            jsonl_file.write(json.dumps(item) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_jsonl('clustered_jsonl/robert_fico_data.json', merged_fico)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge toppic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_post_ids(posts):\n",
    "    return [post['id'] for post in posts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def open_data(fname: str):\n",
    "    jsonl_data = load_jsonl(f'clustered_jsonl/{fname}l')\n",
    "    json_data = load_json(f'json_data_id/{fname}')\n",
    "\n",
    "    post_ids = get_post_ids(json_data)\n",
    "    n_posts = len([post for post in jsonl_data if post['id'] in post_ids])\n",
    "\n",
    "    dataset_info = {\n",
    "        'name': \" \".join(fname.split('_')[: -1]),\n",
    "        'samples': len(jsonl_data),\n",
    "        'n_posts': n_posts,\n",
    "        'post_ids': post_ids\n",
    "    }\n",
    "\n",
    "    return jsonl_data, dataset_info"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Politicians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fico_samples, fico_info = open_data(\"robert_fico_data.json\")\n",
    "matovic_samples, matovic_info = open_data(\"igor_matovic_data.json\")\n",
    "ekalinak_samples, ekalinak_info = open_data(\"erik_kaliňák_data.json\")\n",
    "caputova_samples, caputova_info = open_data(\"zuzana_čaputová_data.json\")\n",
    "kotleba_samples, kotleba_info = open_data(\"marian_kotleba_data.json\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Memes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "zomri_samples, zomri_info = open_data(\"zomri_data.json\")\n",
    "emefka_samples, emefka_info = open_data(\"emefka_data.json\")\n",
    "ok_prv_samples, ok_prv_info = open_data(\"ok,ale_ideš_prvý_:d_data.json\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "eva_samples, eva_info = open_data(\"eva_-_hriešne_dobrá_data.json\")\n",
    "aktuality_samples, aktuality_info = open_data(\"aktuality_data.json\")\n",
    "dennikN_samples, dennikN_info = open_data(\"denník_n_data.json\")\n",
    "tvJOJ_samples, tvJOJ_info = open_data(\"televízia_joj_data.json\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Famous people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "peter_marcin_samples, peter_marcin_info = open_data(\"peter_marcin_data.json\")\n",
    "sajfa_samples, sajfa_info = open_data(\"sajfa_data.json\")\n",
    "jan_kolenik_samples, jan_kolenik_info = open_data(\"ján_koleník_data.json\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sport24_samples, sport24_info = open_data(\"šport24_data.json\")\n",
    "dominika_cibulkova_samples, dominika_cibulkova_info= open_data(\"dominika_cibulkova_data.json\")\n",
    "hetrik_samples, hetrik_info = open_data(\"hetrik_data.json\")\n",
    "RTVSsport_samples, RTVSsport_info = open_data(\"šport_v_rtvs_data.json\")\n",
    "sagan_samples, sagan_info = open_data(\"peter_sagan_data.json\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis & visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_scraped_data(toppic: str, samples: list, names: list, posts: list):\n",
    "    fig, ax = plt.subplots(\n",
    "        nrows = 1,\n",
    "        ncols = 2,\n",
    "        figsize = (17, 5)\n",
    "    )\n",
    "\n",
    "    fig.suptitle(f'Number of extracted records on toppic: {toppic}', fontsize=20)\n",
    "\n",
    "    # first barh chart\n",
    "    \n",
    "    for axis in ax:\n",
    "        axis.set_ylabel('Facebook profiles')\n",
    "        axis.set_xlabel('Number of samples')\n",
    "        \n",
    "    ax[0].title.set_text(\"All samples\")\n",
    "    ax[0].set_xlim(0, max(samples) * 1.2)\n",
    "\n",
    "    for idx, val in enumerate(samples):\n",
    "        ax[0].text(val, idx, str(val), color='black', va='center')\n",
    "\n",
    "\n",
    "    ax[0].barh(\n",
    "        y = names,\n",
    "        width = samples,\n",
    "        edgecolor = BLACK,\n",
    "        color = [BLUE, GREEN, AQUA, ORANGE, YELLOW]\n",
    "    )\n",
    "\n",
    "    # second barh chart\n",
    "    \n",
    "    ax[1].title.set_text(\"Posts\")\n",
    "    ax[1].set_xlim(0, max(posts) * 1.2)\n",
    "\n",
    "    ax[1].barh(\n",
    "        y = names,\n",
    "        width = posts,\n",
    "        edgecolor = BLACK,\n",
    "        color = [BLUE, GREEN, AQUA, ORANGE, YELLOW]\n",
    "    )\n",
    "\n",
    "    for idx, val in enumerate(posts):\n",
    "        ax[1].text(val, idx, str(val), color='black', va='center')\n",
    "\n",
    "    plt.subplots_adjust(wspace=0.5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def execute_visualisation(toppic: str, info_list: list):\n",
    "\n",
    "    if toppic == None:\n",
    "        each_sample = np.empty(0, dtype=np.int32)\n",
    "        each_post = np.empty(0, dtype=np.int32)\n",
    "        names = np.array([\n",
    "                'Politicians',\n",
    "                'Memes',\n",
    "                'Media',\n",
    "                'Famous people',\n",
    "                'Sports'\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        for toppic in info_list:\n",
    "            each_sample = np.append(each_sample, sum([profile['samples'] for profile in toppic]))\n",
    "            each_post = np.append(each_post, sum([profile['n_posts'] for profile in toppic]))\n",
    "\n",
    "        show_scraped_data('By toppic', each_sample, names, each_post)\n",
    "    else:\n",
    "        each_sample = np.array(\n",
    "            [item['samples'] for item in info_list]\n",
    "        )\n",
    "\n",
    "        names = np.array(\n",
    "            [item['name'] for item in info_list]\n",
    "        )\n",
    "\n",
    "        each_post = np.array(\n",
    "            [item['n_posts'] for item in info_list]\n",
    "        )\n",
    "\n",
    "        show_scraped_data(toppic, each_sample, names, each_post)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scraped data visualisation\n",
    "Those graphs represent how many samples we were able to extract from Facebook, those data are cleaned and clustered based on their semantical similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Politics\n",
    "politics_info_list = [fico_info, matovic_info, ekalinak_info, caputova_info, kotleba_info]\n",
    "execute_visualisation('Politics', politics_info_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Memes\n",
    "memes_info_list = [zomri_info, emefka_info, ok_prv_info]\n",
    "execute_visualisation('Memes', memes_info_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Media\n",
    "media_info_list = [eva_info, aktuality_info, dennikN_info, tvJOJ_info]\n",
    "execute_visualisation('Medias', media_info_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Famous people\n",
    "fame_info_list = [peter_marcin_info, sajfa_info, jan_kolenik_info]\n",
    "execute_visualisation('Famous people', fame_info_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mTfM9LFu2bMbrr79u/PLLL0ZkZKSxefNmY86cOUb//v2NfPnyUXQHgMcYRXcAALKwpIrup0+fNgux9evXT7TMoy66J1douHbtmrkuPz8/w2KxGBMnTkzU7tSpU0a2bNkMSUbr1q2TjCXhuipVqpRkcWTmzJlmm06dOiWa/+uvv5rzp0yZkuR6bty4YTRo0MDc7vuvWrz/auzk+kmrM2fOmAXGvHnzJvllQVRUlHm88+XLZ1V0jpfSsUqPb775xixOJPcFyPnz580vJ2rVqpVoflxcnFn4cXJyMjZv3mzOa9++vVnY+/XXX5PsP6Urfu+XsECaN29e4+jRo8m2jYuLMw4cOJBif6NGjTLj+/vvvxPNj4qKMr/cKVasmHH8+PFk+0rqWKb1tXf27FnDx8fHkGQ8//zzyV49O2LECLOYG1/kjbdr1y4z1uReMzNmzLB6Pj9s0V2S4eXlZURHRyfbtnv37uZz9f4vGuIlfM6PGDEi0fx27dqZ6/voo4+SXdeVK1eM8+fPW00bOnSouezbb7+daJm4uDgjNDTUbPPVV1891PYm/HXPnDlzEs2/fPlyoqv+71enTh1DuvcFZ3LPBcMwkr0yPzUJY0zqPe3mzZtG/fr1zTZJfbGY8LVYsGBB459//knUZv369eYXSQMGDHigWNNytXe8L774wmzbo0cPIy4uLlGb+NdQckXXhO9HaTmGDg4OSX7ZGf/aj98/p06dStTmt99+M6+irlq1arq3fdKkSeb8du3aGTdv3kxyv8TGxiZ5fGxRdL969aoREBBg9uPk5GQ0b97cGD9+vLF+/Xrj2rVraern/s/a999/P1GbO3fuGE2aNDHb3P+ltGEYZmHe0dHRWLlyZaL558+fN0qVKmUeu6S+DEp47MqWLWsWzpOS8JwtqV/ZxFuzZo3ZLqWi+p07d4xLly4lOx8AkLVRdAcAIAtLquhuGIbxxhtvmNN/++03q2UeddE9MDAwyUKwYfxfEVOS8dRTTyW7rh49ehiSjOzZsyc5P2HyvWPHjmT7adasmZno31/UiC+mJxzSJCl79+4113V/YThhIaBBgwYp9pMWH330kdnfvHnzkm333nvvme2SGnrAFkX3uLg4c+iEIUOGpNh2+fLlZjxJFadPnDhh+Pn5GdL/XUE6derUFL+kifegRfeZM2emvpGpuHv3rjksxvjx4xPNjx+2wWKxGFFRUenuP62vvfir6fPly5ds4cww7hVk8uXLl2RxOuGvOtLymrFV0f2dd95Jtl1MTIzh6OiYaqHJMAzj9ddfN79MSeivv/4yC7fJXd2anJs3b5pXW5cuXTrJq9gNwzAuXbpkPn9LlSqVaH5at/fUqVPm9rZs2TLZdlu3bk2x6B5/NXxyv2h4GCdOnDBjbNq0abLtYmJizIJw8+bNE81P+FpMauipeE899ZQhyahYseIDxZueonvJkiUN6d4viC5fvpxkmzt37pi/tsqePXui11vC96O0HsNXXnkl0fyEhdv58+cn20/CX/rcf7V7StseGxtr5M+f35Bk5M+fP9UhspJii6K7YdzbF7lz57Z6TicswletWtV45513kiz8x7v/V2VJfWFiGIZx/Phxc6ivFi1aJIojvo++ffsmu64NGzaY7V5++eVE8xMeu5SGrzOMtBfdw8PDzXYU1QHAfnEjVQAAHkPDhg1TtmzZJEkjR47M1Fjat28vZ2fnJOeVL1/e/LtLly7J9hHf7sKFCyne4K1s2bKqXLlysvOff/55SdLdu3etbmR4+fJl8/+OHTsmu7wklSxZUv7+/pKkzZs3J9suNDQ0xX7SIv4mir6+vmrfvn2y7Xr37p1oGVvbu3evDh06JCn1fVS3bl3z76T2Ud68efXtt99Kkg4dOqTu3bvrtddekySVKVNGH374oa3CliS5uLioU6dO6VomLi5OJ0+e1P79+/Xnn3/qzz//1L59+5Q/f35J0s6dOxO1j78JZEhIiCpWrGib4JOwePFiSVLLli3l6uqabDsnJyfVqFFDUuLjEP88SetrxlZSel0sW7ZMsbGx8vDwULNmzVLsJ/45dvLkSaubMy5btsy86fKgQYPSFVtkZKT5/vLcc8/J0dExyXbe3t7q3LmzpHuvi1OnTiXbZ0rbu3btWvMmj7169Uq2XbVq1VS6dOlk5+fJk0eStGTJEv3333/JtnsQERERZowvvPBCsu2CgoL09NNPJ1rmfr6+vmrRokWy/cQ/F1O60bItnDx5Uvv27ZMkde7c2fy8vJ+Tk5N5bC5cuKCoqKhk+0zrMUzpPTp79uxq06ZNsvMTvh7T814fHR2tf/75R5L04osvysvLK83LxgsLC5Nx76K8VG+0npJq1app7969evvttxUYGGg17+7du9q+fbtGjRqlokWLaty4can217Nnz2RvCp4/f341btxYUuLnZcL9l9Jzu1atWipZsmSiZe4XGBioOnXqpBpvWsS/piWZNxMHANgfiu4AADyG/Pz8NHDgQEnSxo0btXLlykyLpVixYsnO8/X1TXe7K1euJNuuatWqKcZSrVo18+/du3ebf//xxx+Ki4uTJHXr1k0WiyXFR3xh699//012XeXKlUsxlrT4888/JUmVKlVK9osLScqdO7eCgoKslrG1HTt2mH/XqFEjxf2TsKCT3D5q166dWehYsmSJrl69KldXV4WHh6dYSH4QwcHBcnNzS7WdYRiaPXu26tevLy8vL+XLl08lSpRQ2bJlzUd0dLQkJSpuxsTEmAVbWxVekhIbG2vG8PXXX6f6XJ0/f74k6+Nw69YtHThwQFL6XjMPy8vLS4ULF052fvxz7Pr163Jyckpxu1q2bGkul3Db/vjjD0mSs7OznnrqqXTFl/C1U7169RTbJpyf3Gsute1N+B70MMehZ8+ekqSDBw+qaNGiev755zV37lyzyPowHmSfXL9+PdmieXBwsBwckk8xc+TIISnl93lbsPWxltJ+DP/++2/dvn07yTYVK1aUk5NTsn1UqFBBLi4ukqyfP6mJf11IGfv+lFY5cuTQu+++q2PHjmnPnj369ttv1bdvX5UtW9Zsc/PmTb3xxhsaPXp0in2ldb9fu3bN6nkZfyxdXFxUoUKFFPuIfw4cOHAg2WNni8/8eLVr1zbfOwYOHKhq1arpgw8+0MaNG5NdPwDg8UPRHQCAx9TgwYPNYnVqSWtG8vDwSHZewuJLWtsldwWlJOXKlSvFWHLnzm3+ff78efPvM2fOpLhccq5fv57svOzZsz9QnwnFx5jadklSQECA1TK2lhH76NNPP7Uq0I8aNcqmhYt4aTkWN2/eVIsWLfTss88qIiJCN27cSLH9/fMTFuETXqVoa+fPn9fdu3fTvVzC43DhwgXzavD0vGYeVsIvz5Jii+dY/HHIkSOHWZxMq4SvndT2S/zr7f7lEkpte9OzvpSOw/PPP68RI0bIyclJly5d0rRp09S9e3cFBgaqaNGiGjJkyANfOW7rfZLS+7z0f+/18V+CZhRbb1da+ok/hoZh6MKFCw/Uh5OTk/nFRHre6x/V+9ODKFWqlHr37q3Jkydr165d2r9/v9XV/u+//76OHDmS7PIP+rkf/3eOHDlS/KJD+r/nQErHzhaf+fGcnZ21ZMkS8wr77du3a8SIEapdu7Z8fX3VtGlTzZkzJ8XzIQBA1pfypw8AAMiyfH19NXjwYI0aNUpbt27V0qVLra4OtUfJ/cQ8NQkT16+//lo1a9ZM03IpJdnJDU3xIB50u2wp4T5asmSJeWV9alIqiEybNk1Xr141/1+9erWGDx9u8+1Ny7EYO3asOTxMvXr19Morr6hSpUoKCAiQu7u7WQysW7eu1q9fbxatH7WEx6F3797msDypSa4A/SifW6kdh/ht8/f319q1a9Pcb6FChR4qrqTYYr+k5z3gYdc3duxY9enTR+Hh4VqzZo22bNmi69ev69ChQ5owYYI+//xzffbZZ+rbt+8DryMrvA9lBFttly36sdd9nB7FihXTggULVLduXW3cuFF3797VwoULkx0u6mH3ma32uS0/86V7X0bs3r1bS5Ys0ZIlS/T777/r4MGDunHjhlauXKmVK1dqwoQJWr58eZq+mAcAZD0U3QEAeIwNHDhQkyZN0rlz5zR69Og0Fd3TeqXhtWvXbBKjLZ0+fTrN8+OvFpTuDccTz8PDQ2XKlLF9cA8gR44cOnXqVKrbJf3fEBsJt8uWEu4jX1/fh95He/fu1RtvvCHp3hjZly9f1tq1a/XJJ59o6NChD9V3ehmGoSlTpki6N/TCb7/9luwQGMldXRo/zr+kFMf4flgJj69hGA90HBJegZ2e10xGi3+OXblyRSVLlnygIlb8cTh//rxu376drqvdE+7b06dPpzjkVcIhbR70NZfwS7vTp08nGt86obQch4IFC2rEiBEaMWKE7ty5o+3bt+uHH37Q119/rZs3b+rll19W9erV03W/gfv3SUox2mKfPCr3b1dK0rpdaT2GFosl2S9sU4vl7t27Vldop9X9708lSpRI87KZwcHBQc8//7w2btwo6d7QSclJ7bWa3Od+/N/nzp3T3bt3U7zaPf45kNKxywiOjo5q27at2rZtK+nesVuxYoW+/PJLRUZGKjIyUi+99JIWLlz4yGICANgOw8sAAPAYy5Ytm4YNGyZJioqKSlNiFn9DueR+Qh3v77//fvgAbWz79u1pnp+wWFmhQgXzarf4JD8riI8xKioqxSFFzpw5o6NHj1otY2sJC3UPu49u376t0NBQ3bx5Ux4eHtq8ebM5Zu7bb7+tXbt2JbtsRlwJev78ebOo0qlTp2QL7levXtX+/fuTnFeoUCGzmP37778/UBxp2TYXFxfzhowPehzc3NwUHBwsKX2vmYwW/xy7deuW1T0E0qNSpUqSpDt37qR4o+OkJHztbN26NcW227ZtS3K59Eg4frWtj4Ozs7Nq1qypiRMnas6cOZLufUkTP8Z/Wj3IPvHw8EhxLPuMlNb3h4w41mk9hsHBwcl+GRQdHZ3ie/3OnTvNMb3T87yLf11ID/7+9KjlzZvX/Dul45rW/X7/8zJ+/92+fdu8T0Zy4p8DKR27tHjYz688efKoV69e2rx5s3lMly5dmupwaACArImiOwAAj7n+/fubPz0ePXp0qsNixA/VEBUVlWzbPXv2pFgYzSy7d++2umHc/b777jtJ964eCwkJMafnzJnTvOninDlzdPbs2QyNM60aNWokSbp48aIWLFiQbLupU6eaxyp+GVurVKmS8ufPL0n65ptvdPPmzQfu6+233zaLHJ9++qlKlSql2bNny8vLS7du3VJoaKhu3bqV5LIJb4iaXJv0SljkSukXHFOmTEm2IObg4KAWLVpIktatW5fi8zA58duW2na1bt1akvTXX3898E2S458naX3NPAqtWrUyi1ITJ058oD5atGjxwH1UrlzZ/OJkxowZyf7a58qVK/rhhx8k3RsC4kHHyK5fv755Nf+MGTOSbbd9+/aHukFyw4YNzb/vvwFwakJCQswYU3ouHDt2TKtWrUq0zKOW1veHvHnzmuNl//DDD1bDXCUUGxur6dOnS7r3y4SExev7pfUYpvQeff78eS1ZsiTZ+QmPQXre68uXL29ehT9lypRktzejpWdYroRfvKX0Jc6sWbOS7ffEiRP69ddfJSV+Xibcfyk9tzdv3qy9e/cmWuZB2Orzy9nZWfXq1ZN07/Mr/ibeAIDHC0V3AAAec56enuYwHrt379by5ctTbB+fyJ08eVJz585NNP/KlSt64YUXbB+ojfTp0yfJwumcOXPMbW/btm2iQtnbb78tSbp8+bI6duyYYhJ769Ytffnllw9VeE6LXr16mTceHDJkiE6cOJGozc6dO/X+++9LkvLly2f+DN3WHBwcNGLECEnS4cOH1aNHjxSLBpcvX9YXX3yRaHpERIQ++eQTSfeKx3369JEkFS1a1CyS/vnnn3rzzTeT7DfhcTt06NADbcv9cubMaRZb586dm+R2bd++XSNHjkyxn6FDh8rBwUGGYahr1676559/km2b1Lz4bUttu1577TXzBrS9evXSnj17Umy/bNmyRF+SvfTSS2ZxOrnXTHh4eKrvF7ZUvHhxderUSZI0b948TZgwIcX2MTExid6jihUrpnbt2kmSFi1apI8//jjZ5a9du2b1ix5XV1f17t1b0r3n4LvvvptoGcMw1L9/f7N43b9//zRsWdLy5Mlj3jBy8eLFZiE/oatXr+qll15KsZ/Zs2eneHV0fNFRSv/493nz5jX35y+//JJkYfn27dt6/vnndefOHUkPt08elp+fn3klcmqvo1deeUWSdPbsWQ0YMCDJNmPGjDELri+++KJcXV2T7S8tx9DBwSHV4zl48OAkh5lZt26dvvnmG0n3viCqWrVqiv0k5ODgYP7q7Z9//lGPHj3MK+bvFxcXp5MnTyaaHhYWJovFIovFYn4RkV5jxozR66+/nmT/Ce3cuVPjx483Y2/VqlWybaOjo5N8nd+9e1cvvviiuZ39+vWzml+tWjVVqVJFkvTtt99qzZo1ifq4dOmS1bG7v4/0Suvn1/r161McUuf27dtat26dJMnLy0s5c+Z8qLgAAJnEAAAAWdbatWsNSYYkY9q0acm2u379upEnTx6zbfwjKWfOnDG8vb0NSYabm5sxZswYY8uWLcbWrVuNr776yihatKjh5uZmVKxY0ZBkFCxYMFEfMTExaYorYfxr165Ntt20adPMdjExMYnmx8+rUqWKIckoUaKEMW3aNGPHjh3GmjVrjH79+hkODg6GJCNbtmxJ9mEYhvHaa6+ZfQUEBBhhYWHG6tWrjT/++MPYsGGDMX36dOOFF14wsmfPbkgyrly5kq44H8SXX35p9pk7d27j008/NbZu3Wps3LjRGDNmjOHl5WVIMiwWi7Fs2bIk++jZs2eyxyo94uLijHbt2pnxFClSxBg3bpwRERFh/PHHH8a6deuMr7/+2ujWrZvh6elp+Pn5WS1/4cIFo0CBAua2nDlzJtE64vu3WCzG6tWrE80/cOCAuf7GjRsb69atM/7++2/jwIEDxoEDB4w7d+6YbevVq2dIMurVq5fqtr3yyitWz6M5c+YY27dvN1avXm0MHjzYcHNzM/z9/Y1ixYql2Oe7775r9uPr62u89dZb5nNo7dq1xqeffmrUqVPHCAkJSbTsW2+9ZS77wQcfGNHR0eZ2/fPPP1Ztf/rpJ8NisZiv0759+xo///yzERkZaWzZssWYP3++8frrrxuFCxc2JBlLlixJtL7+/fub67v/NdO3b1/DwcHBfE1JMkaPHp3qfkxKep5/586dM2OWZNStW9eYMmWKsXnzZiMqKspYtWqVMX78eKNRo0aGg4OD0aFDh0R9/Pvvv0bevHnNPho0aGDMnDnT2LZtm7F9+3bjxx9/NF5++WUjR44cid53Ll++bLX+Dh06GEuXLjUiIyON+fPnGyEhIea8GjVqGHfv3n2o7Y2JiTGyZctmSDIcHR2Nl19+2fjtt9+MHTt2GN999535fEt4HO4X/3rq16+fMWvWLGPTpk1GVFSU8csvvxiDBw823N3dDUmGl5eXcezYsVRjut/x48fN9zwHBwejd+/exqpVq4wdO3YYs2fPNipUqGDG1rlz5yT7SOtrcfTo0Sl+PqVFrVq1DEmGn5+fMWfOHGPv3r3m6+jcuXNmu7t37xo1atSwep7Mnz/fiIyMNJYuXWq0b9/e6r3u/vf7++OtUqVKksewePHiZptXX301yZgLFixoSDLKly9vODs7G/ny5TO++OILY9u2bcb69euN4cOHG25uboYkw8nJydiyZUuiPlL73I2NjTWefvpps02xYsWMiRMnGhs2bDCioqKM5cuXG6NGjTKCg4OTfK0n3NaUPtdTMmTIEPO53rhxY+PDDz80VqxYYURGRho7duwwfvrpJ6NPnz6Gq6urua7XXnstUT8JP2vjXxvdunUzfvnlFyMyMtKYN2+eUa1aNbNNq1atkoznjz/+MFxcXAxJhouLizFkyBAjIiLC2L59u/HNN99YvRe8/vrrSfYRf+x69uyZpn2QP39+Q5JRqFAh4+effzb++usv8/l5+fJlwzDu7WsHBwejXr16xrhx48x9tGHDBuO7776z2rak9g8A4PFA0R0AgCwsrUV3wzCMzz//PE1Fd8MwjB9++MFwdHRM1F6S4e7ubvz4448pFpYyq+g+evRoq8LA/Q9vb28jIiIi2fXExcUZY8aMMZycnJLtI/7h6elpXL9+PV1xPqixY8eaXxok9XB1dTVmzJiR7PK2KrobhmHcvn3b6Nevn1nwTelRqFAhq2W7detmzkvuC4KzZ8+aXxDly5fPOH/+fKI2nTt3TnadCfd7eoruFy9etCoe3v/IkSOHsW7dujT1OXbs2FSfQ0kt/88//xg5cuRIc/vFixcn2z7hw8HBwfjtt98SLX/79m2rwmJSx+/QoUNWr68Hkd7n36lTp4w6deqkul2SjF69eiXZx6FDh4wyZcqkunxS7zsxMTFGiRIlUlyuVq1aVgXch9netWvXmoX3pB6jRo1KsRidlv3k4+Nj/PLLL2mKJylRUVFWX2Qk9Wjfvr1x48aNJJd/lEX3pUuXJvv+dP9z+Ny5c2aRPrlHyZIljSNHjqQa7+HDh41ChQol20+HDh2svhRMKGHh9ttvv032/cPFxcWYO3dukn2k5XP32rVrRseOHVN9vmRU0X38+PHJnlsk9b41aNAgIzY2NlE/CT9ro6KizIsAknutxhezk7Jy5UrzQoPkHq+88kqScRhG+ovuX331VbLrid+vKZ3HJHy0adMm0XkIAODxwfAyAADYiRdffNEc0zU1nTp10qZNm9SuXTvlzJlTLi4uCgwMVM+ePbV9+3Z17Ngxg6N9cGFhYVqxYoVatGih3Llzy8XFRUFBQXr55Ze1Z88ec/icpFgsFo0aNUp///23Xn/9dVWpUkU5cuSQo6OjsmXLplKlSik0NFQzZszQqVOn5O7u/ki2acSIEfrjjz/04osvqkiRInJ3d5enp6dKliyp1157TX/99Zd69OjxSGJxdnbWV199pZ07d+rVV19V2bJl5ePjI0dHR/n4+KhChQp64YUXNH/+fO3bt89cbs6cOeZQIP369VPz5s2T7N/f31/Tpk2TxWLRiRMn1Ldv30RtZs+erXHjxqlatWry8fFJ9san6eHj46ONGzfq3XffVdmyZeXm5iYvLy+VLFlSQ4cO1c6dO1W3bt009TVixAjt3btXAwcOVJkyZeTt7S0nJyflzJlT9erV03vvvadZs2YlWi5fvnzatm2bXnjhBRUtWtRq/N+ktGrVSjExMRo/frwaNGig3Llzy9nZWe7u7ipUqJBatmypCRMm6MiRI6pfv36i5Z2dnfXTTz9p1qxZqlOnjnx8fOTh4aGSJUtqxIgRioyMzJQbYgYEBOj333/X0qVLFRoaqsKFC8vDw0POzs7KmTOnatasqSFDhmjdunXJjsVcuHBhRUdHa/r06WrRooXy5Mlj7ptixYqpR48e+vnnn1WnTp1EywYFBWnnzp364osvVK9ePfn5+cnZ2Vm5c+dW06ZNNWvWLP3+++/KkSOHTbY3JCREe/bsUb9+/VSwYEG5uLgod+7catGihVasWKExY8akuPyff/6pjz76SK1atVKpUqXk5+cnR0dH+fr66qmnntLo0aO1f/9+NW3a9IFjrFixovbv368PPvhA1atXl6+vr1xcXJQ3b161b99eixcv1k8//ZTqc/ZRaNGihdasWaM2bdoob968cnZ2TrZtjhw59Pvvv2vmzJlq2rSp+Rry8/NTSEiIvvjiC0VHR6tgwYKprrdQoUKKjIzUiBEjVLJkSXl4eMjHx0d169bV7NmzNX/+fDk5OaXaT+/evbV+/Xp17txZefPmlYuLi/Lly6cePXrojz/+UNeuXdO1PxLy8PDQjz/+qN9++03PPvusChUqJHd3d/MzvlWrVvr66681ZMiQB15HSoYMGaJTp05pxowZeuG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      "text/plain": [
       "<Figure size 1700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sports\n",
    "sport_info_list = [sport24_info, dominika_cibulkova_info, hetrik_info, RTVSsport_info, sagan_info]\n",
    "execute_visualisation('Sports', sport_info_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# By toppic\n",
    "execute_visualisation(None, [politics_info_list, memes_info_list, media_info_list, fame_info_list, sport_info_list])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Language identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lang_identification_pipeline = pipeline(\"text-classification\", model=\"papluca/xlm-roberta-base-language-detection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 333/8050 [00:17<05:52, 21.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expanded size of the tensor (535) must match the existing size (514) at non-singleton dimension 1.  Target sizes: [1, 535].  Tensor sizes: [1, 514]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██▌       | 2113/8050 [01:53<04:38, 21.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expanded size of the tensor (524) must match the existing size (514) at non-singleton dimension 1.  Target sizes: [1, 524].  Tensor sizes: [1, 514]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 6215/8050 [05:33<01:25, 21.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expanded size of the tensor (554) must match the existing size (514) at non-singleton dimension 1.  Target sizes: [1, 554].  Tensor sizes: [1, 514]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8050/8050 [07:14<00:00, 18.53it/s]\n"
     ]
    }
   ],
   "source": [
    "cibulkova_idxs = np.empty(0, dtype=np.int32)\n",
    "\n",
    "for idx, text in enumerate(tqdm(dominika_cibulkova_samples)):\n",
    "    try:\n",
    "        identified_text = lang_identification_pipeline(text['text'])[0]\n",
    "        if identified_text['label'] in ['en', 'es'] and identified_text['score'] > 0.70:\n",
    "            cibulkova_idxs = np.append(cibulkova_idxs, idx)\n",
    "    except Exception as err:\n",
    "        print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 6049/18515 [05:07<09:39, 21.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expanded size of the tensor (1268) must match the existing size (514) at non-singleton dimension 1.  Target sizes: [1, 1268].  Tensor sizes: [1, 514]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 51%|█████     | 9469/18515 [07:58<06:49, 22.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expanded size of the tensor (1764) must match the existing size (514) at non-singleton dimension 1.  Target sizes: [1, 1764].  Tensor sizes: [1, 514]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|██████▌   | 12208/18515 [10:15<05:12, 20.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expanded size of the tensor (1036) must match the existing size (514) at non-singleton dimension 1.  Target sizes: [1, 1036].  Tensor sizes: [1, 514]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 71%|███████   | 13139/18515 [11:02<04:08, 21.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expanded size of the tensor (959) must match the existing size (514) at non-singleton dimension 1.  Target sizes: [1, 959].  Tensor sizes: [1, 514]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72%|███████▏  | 13298/18515 [11:10<03:59, 21.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expanded size of the tensor (893) must match the existing size (514) at non-singleton dimension 1.  Target sizes: [1, 893].  Tensor sizes: [1, 514]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18515/18515 [15:34<00:00, 19.81it/s]\n"
     ]
    }
   ],
   "source": [
    "sagan_idxs = np.empty(0, dtype=np.int32)\n",
    "\n",
    "for idx, text in enumerate(tqdm(sagan_samples)):\n",
    "    try:\n",
    "        identified_text = lang_identification_pipeline(text['text'])[0]\n",
    "        if identified_text['label'] in ['en', 'es'] and identified_text['score'] > 0.70:\n",
    "            sagan_idxs = np.append(sagan_idxs, idx)\n",
    "    except Exception as err:\n",
    "        print(err)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping english and spannish data from famous slovak sportsmen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cibulkova_idxs_reversed = cibulkova_idxs[::-1]\n",
    "for idx in cibulkova_idxs_reversed:\n",
    "    dominika_cibulkova_samples.pop(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sagan_idxs_reversed = sagan_idxs[::-1]\n",
    "for idx in sagan_idxs_reversed:\n",
    "    sagan_samples.pop(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('clustered_jsonl/dominika_slovak.jsonl', 'w') as f:\n",
    "    for item in dominika_cibulkova_samples:\n",
    "        json.dump(item, f)\n",
    "        f.write('\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get number of posts in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_num_of_posts(data: list, info: dict):\n",
    "    return len([item for item in data if item['id'] in info['post_ids']])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data merging"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Meme data merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "meme_samples_final = []\n",
    "\n",
    "meme_samples_final += zomri_samples\n",
    "meme_samples_final += emefka_samples\n",
    "\n",
    "# ok ale ides prvy dont consist any type of criticism or hate\n",
    "post_meme = get_num_of_posts(zomri_samples, zomri_info) + get_num_of_posts(emefka_samples, emefka_info)\n",
    "meme_share = len(meme_samples_final)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Media data merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "media_samples_final = []\n",
    "\n",
    "eva_hate = eva_samples[250: 387]\n",
    "\n",
    "eva_hate_1 = eva_samples[700:]\n",
    "\n",
    "media_samples_final += eva_hate\n",
    "media_samples_final += eva_hate_1\n",
    "media_samples_final += aktuality_samples\n",
    "media_samples_final += dennikN_samples\n",
    "media_samples_final += tvJOJ_samples\n",
    "\n",
    "post_media = get_num_of_posts(eva_hate, eva_info) + get_num_of_posts(eva_hate_1, eva_info) + get_num_of_posts(aktuality_samples, aktuality_info) + get_num_of_posts(dennikN_samples, dennikN_info)  + get_num_of_posts(tvJOJ_samples, tvJOJ_info)\n",
    "media_share = len(media_samples_final)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Famous people data merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "famous_samples_final = []\n",
    "\n",
    "# jan_kolenik_samples he has just possitive conversations on his profile\n",
    "famous_samples_final += sajfa_samples[:240]\n",
    "# peter_marcin_samples has also positive conversations\n",
    "\n",
    "post_fame = get_num_of_posts(sajfa_samples[:240], sajfa_info)\n",
    "fame_share = len(famous_samples_final)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sport_samples_final = []\n",
    "\n",
    "sport_samples_final += sport24_samples\n",
    "sport_samples_final += RTVSsport_samples\n",
    "\n",
    "post_sport = get_num_of_posts(sport24_samples, sport24_info) + get_num_of_posts(RTVSsport_samples, RTVSsport_info)\n",
    "sport_share = len(sport_samples_final)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Politician data merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "politican_samples_final = []\n",
    "\n",
    "# fico_samples\n",
    "politican_samples_final += ekalinak_samples\n",
    "politican_samples_final += kotleba_samples\n",
    "politican_samples_final += fico_samples[:50000]\n",
    "\n",
    "post_politics = get_num_of_posts(ekalinak_samples, ekalinak_info) + get_num_of_posts(kotleba_samples, kotleba_info) + get_num_of_posts(fico_samples[:50000], fico_info)\n",
    "politics_share = len(politican_samples_final)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie chart of data ratio in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(\n",
    "    [meme_share, media_share, fame_share, sport_share, politics_share],\n",
    "    labels=['Memes', 'Medias', 'Famous people', 'Sports', 'Politicians'],\n",
    "    autopct='%1.1f%%',\n",
    "    wedgeprops= {\n",
    "        'edgecolor': 'BLACK'\n",
    "    },\n",
    "    colors=[BLUE, GREEN, AQUA, ORANGE, YELLOW]\n",
    ")\n",
    "\n",
    "plt.title('ratio of toppics in final dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = []\n",
    "\n",
    "final_dataset += meme_samples_final\n",
    "final_dataset += media_samples_final\n",
    "final_dataset += famous_samples_final\n",
    "final_dataset += sport_samples_final\n",
    "final_dataset += politican_samples_final\n",
    "\n",
    "final_post_num = post_fame + post_media + post_meme + post_sport + post_politics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset has 171579 samples. 468 are posts out of it.\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset has {len(final_dataset)} samples. {final_post_num} are posts out of it.')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie chart of post ratio in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(\n",
    "    [post_fame, post_media, post_meme, post_sport, post_politics],\n",
    "    labels=['Famous people', 'Medias', 'Memes', 'Sports', 'Politicians'],\n",
    "    autopct='%1.1f%%',\n",
    "    wedgeprops= {\n",
    "        'edgecolor': 'BLACK'\n",
    "    },\n",
    "    colors=[BLUE, GREEN, AQUA, ORANGE, YELLOW]\n",
    ")\n",
    "\n",
    "plt.title('ratio of posts in final dataset')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GDPR protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "GDPR_final = final_dataset.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_mapping = {}\n",
    "idx = 1\n",
    "for sample in final_dataset:\n",
    "    author = sample['author'].lower()\n",
    "    if author not in author_mapping:\n",
    "        author_mapping[author] = f'user{idx}'\n",
    "        idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47254/47254 [00:00<00:00, 2697741.07it/s]\n"
     ]
    }
   ],
   "source": [
    "keys_to_delete = []\n",
    "for key in tqdm(author_mapping.keys()):\n",
    "    if len(key) < 7:\n",
    "        keys_to_delete.append(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in keys_to_delete:\n",
    "    del author_mapping[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 171579/171579 [1:01:55<00:00, 46.18it/s]\n"
     ]
    }
   ],
   "source": [
    "for sample in tqdm(final_dataset):\n",
    "    author = sample['author'].lower()\n",
    "    if author in author_mapping:\n",
    "        sample['author'] = author_mapping[author]\n",
    "    \n",
    "    for key in author_mapping:\n",
    "        sample['text'] = sample['text'].lower().replace(key, author_mapping[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('final.jsonl', 'w') as f:\n",
    "    for item in final_dataset:\n",
    "        json.dump(item, f)\n",
    "        f.write('\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ID appending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 171579/171579 [00:00<00:00, 2332284.88it/s]\n"
     ]
    }
   ],
   "source": [
    "id = 1\n",
    "for item in tqdm(final_dataset):\n",
    "    item['id'] = id\n",
    "    id += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('final_id_v2.jsonl', 'w') as f:\n",
    "    for item in final_dataset:\n",
    "        json.dump(item, f)\n",
    "        f.write('\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment data reducing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_pipe = pipeline(\"text-classification\", model=\"kinit/slovakbert-sentiment-twitter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 344/344 [2:30:05<00:00, 26.18s/it]  \n"
     ]
    }
   ],
   "source": [
    "positive_samples = []\n",
    "negative_samples = []\n",
    "neutral_samples = []\n",
    "\n",
    "# Batch processing for efficiency\n",
    "batch_size = 500\n",
    "batched_texts = [final_dataset[i:i + batch_size] for i in range(0, len(final_dataset), batch_size)]\n",
    "\n",
    "for batch in tqdm(batched_texts):\n",
    "    try:\n",
    "        sentiments = sentiment_pipe([sample['text'] for sample in batch])\n",
    "        for sample, sentiment in zip(batch, sentiments):\n",
    "            sentiment_val = sentiment['label']\n",
    "            if sentiment_val == '1':\n",
    "                positive_samples.append(sample)\n",
    "                continue\n",
    "            elif sentiment_val == '0':\n",
    "                neutral_samples.append(sample)\n",
    "                continue\n",
    "            negative_samples.append(sample)\n",
    "    except Exception as err:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('classified_data/neutral_v1.jsonl', 'w') as f:\n",
    "    for item in neutral_samples:\n",
    "        json.dump(item, f)\n",
    "        f.write('\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging classified data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_jsonl(file_path):\n",
    "    with open(file_path, 'r', encoding='utf-8') as file:\n",
    "        return [json.loads(line) for line in file]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_samples = load_jsonl('classified_data/positive.jsonl')\n",
    "negative_samples = load_jsonl('classified_data/negative.jsonl')\n",
    "neutral_samples = load_jsonl('classified_data/neutral.jsonl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(\n",
    "    [len(positive_samples), len(negative_samples), len(neutral_samples)],\n",
    "    labels=['Positive samples', 'Negative samples', 'Neutral samples'],\n",
    "    autopct='%1.1f%%',\n",
    "    wedgeprops= {\n",
    "        'edgecolor': 'BLACK'\n",
    "    },\n",
    "    colors=[BLUE, GREEN, AQUA]\n",
    ")\n",
    "\n",
    "plt.title('Ratio of records according to the sentimental value of the text')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting samples with swear words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_word(words: list, sample: str):\n",
    "\n",
    "    for word in words:\n",
    "        if re.search(r'\\b' + word + r'\\b', sample.lower()) is not None:\n",
    "            return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "swear_words = (\n",
    "    'kokot',\n",
    "    'kkt',\n",
    "    'koko*',\n",
    "    'pi.',\n",
    "    'pi*'\n",
    "    'pic',\n",
    "    'pič',\n",
    "    'sedl',\n",
    "    'hovn',\n",
    "    'retard',\n",
    "    'bastard',\n",
    "    'idiot',\n",
    "    'kurv',\n",
    "    'geň',\n",
    "    'geno',\n",
    "    'riť',\n",
    "    'rit',\n",
    "    'anal',\n",
    "    'jeb',\n",
    "    'curak',\n",
    "    'čur',\n",
    "    'chuj',\n",
    "    'srack',\n",
    "    'srač',\n",
    "    'hoveda',\n",
    "    'hovada',\n",
    "    'hoväd',\n",
    "    'cigan',\n",
    "    'cigán',\n",
    "    'neger',\n",
    "    'negri',\n",
    "    'blaznov',\n",
    "    'bláznov',\n",
    "    'blazon',\n",
    "    'blázon',\n",
    "    'krist',\n",
    "    'hajz',\n",
    "    'stet',\n",
    "    'štet',\n",
    "    'sex',\n",
    "    'gay',\n",
    "    'gej',\n",
    "    'buzerant',\n",
    "    'buzik',\n",
    "    'tepl',\n",
    "    'hanb',\n",
    "    'hamb',\n",
    "    'nigg',\n",
    "    'uchyl',\n",
    "    'uchil',\n",
    "    'hnus',\n",
    "    'potkan',\n",
    "    'trapn',\n",
    "    'odpad',\n",
    "    'zmrd',\n",
    "    'drog',\n",
    "    'zlatok',\n",
    "    'vymydl',\n",
    "    'mrch',\n",
    "    'fet',\n",
    "    'zbabelec',\n",
    "    'psycho',\n",
    "    'k.k.t',\n",
    "    'maria',\n",
    "    'mariu',\n",
    "    'svin',\n",
    "    'egocent',\n",
    "    'chuda',\n",
    "    'bit',\n",
    "    'nasilnik',\n",
    "    'spodina',\n",
    "    'spo.dina',\n",
    "    'magor',\n",
    "    'tyran',\n",
    "    'tiran',\n",
    "    'boha',\n",
    "    'spina',\n",
    "    'dement',\n",
    "    'hnoj',\n",
    "    'dobil',\n",
    "    'psicho',\n",
    "    'hajzl',\n",
    "    'komediant',\n",
    "    'nechutny',\n",
    "    'sprostak',\n",
    "    'mafia',\n",
    "    'cecky',\n",
    "    'ser',\n",
    "    'vola',\n",
    "    'klam',\n",
    "    'cirkus',\n",
    "    'huba',\n",
    "    'huby',\n",
    "    'trtko',\n",
    "    'trtkat',\n",
    "    'o.e'\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_swear_samples = []\n",
    "pos_swear_samples = []\n",
    "neu_swear_samples = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 111568/111568 [00:18<00:00, 6070.15it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3485"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for sample in tqdm(negative_samples):\n",
    "    if find_word(swear_words, sample['text']):\n",
    "        neg_swear_samples.append(sample)\n",
    "\n",
    "len(neg_swear_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 34083/34083 [00:04<00:00, 7840.59it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "182"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for sample in tqdm(positive_samples):\n",
    "    if find_word(swear_words, sample['text']):\n",
    "        pos_swear_samples.append(sample)\n",
    "\n",
    "len(pos_swear_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 11428/11428 [00:00<00:00, 11980.43it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "79"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for sample in tqdm(neutral_samples):\n",
    "    if find_word(swear_words, sample['text']):\n",
    "        neu_swear_samples.append(sample)\n",
    "\n",
    "len(neu_swear_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_samples_without_swear_words(samples: list, swear_samples: list):\n",
    "    return [item for item in samples if item not in swear_samples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_without_swear = get_samples_without_swear_words(positive_samples, pos_swear_samples)\n",
    "negative_without_swear = get_samples_without_swear_words(negative_samples, neg_swear_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_random_samples_by_len(samples: list, length: int):\n",
    "    random.shuffle(samples)\n",
    "    return samples[:length]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing sample percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11428"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(neutral_samples) # 20 % of the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffled_positive_data = get_random_samples_by_len(positive_without_swear, len(neutral_samples) - len(pos_swear_samples)) # other 20% the same  as neutral data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffled_negative_data = get_random_samples_by_len(negative_without_swear, int((((2 * len(neutral_samples)) / 40) * 60)) - len(neg_swear_samples)) # next 60%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34284"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(shuffled_negative_data) + len(neg_swear_samples)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final dataset merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "classified_data = []\n",
    "\n",
    "def append_final(input_list: list):\n",
    "    for sample in input_list:\n",
    "        classified_data.append(sample)\n",
    "\n",
    "\n",
    "append_final(neutral_samples)\n",
    "append_final(pos_swear_samples)\n",
    "append_final(shuffled_positive_data)\n",
    "append_final(neg_swear_samples)\n",
    "append_final(shuffled_negative_data)\n",
    "\n",
    "random.shuffle(classified_data)\n",
    "\n",
    "id = 1\n",
    "for sample in classified_data:\n",
    "    sample['id'] = id\n",
    "    id += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57140"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(classified_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(\n",
    "    [len(shuffled_positive_data) + len(pos_swear_samples), len(shuffled_negative_data) + len(neg_swear_samples), len(neutral_samples)],\n",
    "    labels=['Positive samples', 'Negative samples', 'Neutral samples'],\n",
    "    autopct='%1.1f%%',\n",
    "    wedgeprops= {\n",
    "        'edgecolor': 'BLACK'\n",
    "    },\n",
    "    colors=[BLUE, GREEN, AQUA]\n",
    ")\n",
    "\n",
    "plt.title('ratio of data in the final dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(\n",
    "    [len(pos_swear_samples), len(neg_swear_samples), len(neu_swear_samples)],\n",
    "    labels=['Positive samples', 'Negative samples', 'Neutral samples'],\n",
    "    autopct='%1.1f%%',\n",
    "    wedgeprops= {\n",
    "        'edgecolor': 'BLACK'\n",
    "    },\n",
    "    colors=[BLUE, GREEN, AQUA]\n",
    ")\n",
    "\n",
    "plt.title('ratio of samples with swear words')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('final_id_v2.jsonl', 'w') as f:\n",
    "    for item in classified_data:\n",
    "        json.dump(item, f)\n",
    "        f.write('\\n')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sentiment",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}