word_counter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dany/.local/share/virtualenvs/cuba-digital-lang-9Kd57kqM/lib/python3.9/site-packages/tqdm/auto.py:22: 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": [
    "from submodulos.tokenizer.custom_tokenizer import SpacyCustomTokenizer\n",
    "from submodulos.data import get_all_text_with_details \n",
    "from submodulos.data.telegram import get_details_text\n",
    "from tools import get_progressbar\n",
    "from datetime import datetime\n",
    "import os\n",
    "\n",
    "texts = get_all_text_with_details(f'{os.getcwd()}/submodulos/data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sorted_by_dict(d):\n",
    "    result = [(key, d[key] )for key in d]\n",
    "    result.sort(key= lambda x: x[1], reverse= True)\n",
    "    return result\n",
    "\n",
    "def print_best_word(list_):\n",
    "    for i in range(20):\n",
    "        print(list_[i], end=' ')\n",
    "\n",
    "def total_len(d):\n",
    "    return sum([d[key] for key in d])\n",
    "\n",
    "def count(tokens, predicate, name= ' count any feature ', map_= lambda x: x.text):\n",
    "    bar = get_progressbar(len(tokens), name)\n",
    "\n",
    "    result = {}\n",
    "    bar.start()\n",
    "    for i, t in enumerate(tokens):\n",
    "        if not predicate(t): continue\n",
    "        try: result[map_(t)] += 1\n",
    "        except: result[map_(t)] = 1\n",
    "        bar.update(i + 1)\n",
    "    bar.finish()\n",
    "\n",
    "    return result\n",
    "\n",
    "def density_time(tokens, predicate, name= ' count any feature '):\n",
    "    bar = get_progressbar(len(tokens), name)\n",
    "\n",
    "    result = {}\n",
    "    bar.start()\n",
    "    for i, t in enumerate(tokens):\n",
    "        if not predicate(t): continue\n",
    "        date = str(datetime.strptime(t.data.split(' ')[0], '%Y-%m-%d'))\n",
    "        try: result[date][t.text.lower()] += 1\n",
    "        except KeyError: \n",
    "            try: result[date][t.text.lower()] = 1\n",
    "            except KeyError: \n",
    "                result[date] = {}\n",
    "                result[date][t.text.lower()] = 1\n",
    "            \n",
    "        bar.update(i + 1)\n",
    "    bar.finish()\n",
    "\n",
    "    return result\n",
    "\n",
    "def density_text_by_time(texts, name= ' count any feature '):\n",
    "    bar = get_progressbar(len(texts), name)\n",
    "\n",
    "    result = {}\n",
    "    bar.start()\n",
    "    for i, t in enumerate(texts):\n",
    "        t, d, _ = t\n",
    "        date = str(datetime.strptime(d.split(' ')[0], '%Y-%m-%d'))\n",
    "        try: result[date] += 1\n",
    "        except KeyError: \n",
    "            result[date] = 1\n",
    "            \n",
    "        bar.update(i + 1)\n",
    "    bar.finish()\n",
    "\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CubaDebate': 9026, 'Facebook': 287, 'Twitter': 109941, 'Telegram': 156440}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_list = {\n",
    "    'CubaDebate': 0,\n",
    "    'Facebook': 0,\n",
    "    'Twitter': 0,\n",
    "    'Telegram': 0\n",
    "}\n",
    "\n",
    "for t, d, n in texts:\n",
    "    text_list[n] += 1\n",
    "\n",
    "text_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[##########################] from 177284 text, tokenizer and tag with date 100%\n"
     ]
    }
   ],
   "source": [
    "nlp = SpacyCustomTokenizer()\n",
    "bar = get_progressbar(len(texts), f' from {len(texts)} text, tokenizer and tag with date ')\n",
    "bar.start()\n",
    "token_list = []\n",
    "for i, data in enumerate(texts):\n",
    "    t, d, n = data\n",
    "    for token in nlp(t):\n",
    "        token.data = d\n",
    "        token.source = n\n",
    "        token_list.append(token)\n",
    "    bar.update(i + 1)\n",
    "bar.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[#####################################################] count natural word 100%\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('vip', 53412) ('contraseña', 23434) ('audio', 23057) ('sinopsis', 22080) ('vence', 21777) ('español', 21388) ('género', 21305) ('bot', 21106) ('canal', 19260) ('nombre', 17408) ('tamaño', 15558) ('caps', 15344) ('subtítulos', 15288) ('barcelona', 15062) ('temp', 14624) ('grupo', 14275) ('resubido', 13240) ('mb', 12450) ('q', 11948) ('años', 11554) "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "natural = count(token_list, lambda x : x.natural_word() and not x.is_stop, ' count natural word ', lambda x: x.text.lower())\n",
    "print_best_word(sorted_by_dict(natural))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[#####################################################] count natural word 100%\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('vip', 51732) ('contraseña', 23325) ('audio', 23066) ('sinopsis', 22080) ('español', 21903) ('vence', 21479) ('género', 21456) ('bot', 21149) ('canal', 19967) ('nombre', 17657) ('tamaño', 15577) ('caps', 15344) ('año', 15343) ('grupo', 14668) ('subtítulos', 14656) ('temp', 14624) ('barcelona', 14226) ('resubido', 13240) ('mb', 12450) ('serie', 12110) "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "lemma = count(token_list, lambda x : x.natural_word() and not x.is_stop, ' count natural word ', lambda x: x.lemma.lower() if x.lemma != None else '')\n",
    "print_best_word(sorted_by_dict(lemma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[###########################################################] count emojis 100%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "856638\n",
      "('📲', 96079) ('🤖', 41498) ('‼', 36668) ('⚜', 34960) ('🖥', 32738) ('❗', 23254) ('📣', 21547) ('📝', 21509) ('🔑', 21274) ('🎭', 19236) ('🔊', 17876) ('🕐', 17346) ('💬', 16946) ('🎬', 16942) ('📼', 16876) ('📀', 14784) ('💎', 14673) ('✅', 12884) ('❤', 12240) ('🔱', 11844) "
     ]
    }
   ],
   "source": [
    "emojis = count(token_list, lambda x : x.is_emoji(), ' count emojis ')\n",
    "print(total_len(emojis))\n",
    "print_best_word(sorted_by_dict(emojis))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[######################################################] count any feature 100%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23662\n",
      "('00pm', 2332) ('30pm', 1053) ('0-0', 970) ('1-2', 728) ('00am', 709) ('45pm', 590) ('1-0', 584) ('mp4', 528) ('0-1', 526) ('11pm', 497) ('1-1', 487) ('1914-1918', 416) ('5-6', 397) ('4pm', 392) ('2-1', 322) ('3-4', 317) ('2pm', 279) ('9-10', 272) ('10pm', 261) ('20pm', 257) "
     ]
    }
   ],
   "source": [
    "date = count(token_list, lambda x : x.is_date() and not x.natural_word() and not x.is_symbol)\n",
    "print(total_len(date))\n",
    "print_best_word(sorted_by_dict(date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[######################################################] count any feature 100%\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "227916\n",
      "('1', 20001) ('10', 13924) ('2', 12037) ('9', 11287) ('3', 10425) ('4', 7556) ('21', 7071) ('5', 6547) ('8', 5774) ('0', 5650) ('11', 5512) ('7', 5285) ('00', 5193) ('20', 4890) ('6', 4790) ('30', 4683) ('12', 4642) ('2021', 3196) ('15', 3143) ('09', 2684) "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "d = count(token_list, lambda x : x.is_digit())\n",
    "print(total_len(d))\n",
    "print_best_word(sorted_by_dict(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[######################################################] count any feature 100%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16896\n",
      "('#forçabarça', 712) ('#fcbarcelona', 648) ('#cuba', 594) ('#somosuh', 568) ('#somosfeu', 519) ('#koemanout', 300) ('#fcb', 294) ('#masia', 288) ('#gratis', 224) ('#vip', 224) ('#anime', 209) ('#elclásico', 180) ('#misterseitan', 152) ('#lalupa', 152) ('#dictadurafcb', 140) ('#goat', 136) ('#barçacelta', 128) ('#', 116) ('#barcelona', 112) ('#vamoscontodo', 111) "
     ]
    }
   ],
   "source": [
    "d = count(token_list, lambda x : x.is_hashtag(), map_= lambda x: x.text.lower())\n",
    "print(total_len(d))\n",
    "print_best_word(sorted_by_dict(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[######################################################] count any feature 100%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "153478\n",
      "('@noticiasbarcelona', 48988) ('@s3spn', 44208) ('@s3youbot', 20240) ('@victornoel89', 3712) ('@curioseo', 1890) ('@lejandrosagua', 1488) ('@', 1319) ('@zona3dcuba', 1218) ('@reiniers94', 1072) ('@mailyn1993', 1056) ('@claudiaflores99', 1040) ('@dphoenixkirito', 864) ('@assiela90', 736) ('@zona3d', 574) ('@r_d_colomina', 512) ('@atds3', 502) ('@supers3plus', 496) ('@dayamy_amy', 416) ('@laligasan', 416) ('@theshowtimes3', 400) "
     ]
    }
   ],
   "source": [
    "d = count(token_list, lambda x : x.is_user_tag(), map_= lambda x: x.text.lower())\n",
    "print(total_len(d))\n",
    "print_best_word(sorted_by_dict(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[######################################################] count any feature 100%\n",
      "[######################################################] count any feature 100%\n"
     ]
    }
   ],
   "source": [
    "d = density_time(token_list, lambda x : x.natural_word() and not x.is_stop)\n",
    "d_plus = density_text_by_time(texts) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "  \n",
    "x = list(d.keys())\n",
    "x.sort()\n",
    "y = [total_len(d[key])/d_plus[key] for key in x]\n",
    "  \n",
    " \n",
    "# creating the bar plot\n",
    "plt.barh(x, y, color ='maroon')\n",
    " \n",
    "plt.xlabel(\"Courses offered\")\n",
    "plt.ylabel(\"No. of students enrolled\")\n",
    "plt.title(\"Students enrolled in different courses\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('results/word-day.json', 'w+') as f:\n",
    "    f.write(json.dumps(d))\n",
    "\n",
    "\n",
    "with open('results/text-day.json', 'w+') as f:\n",
    "    f.write(json.dumps(d_plus))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cuba-digital-lang",
   "language": "python",
   "name": "cuba-digital-lang"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}