OpLexicon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "now = datetime.datetime.now()\n",
    "today = now.strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "oplexicon = open('Lexicon/OpLexicon/lexico_v2.txt', 'r', encoding=\"utf8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-aeef96f6b834>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mpalavra\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mpos_ponto\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mpol_pos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'-?\\d+\\.?\\d*'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mpolaridade\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpol_pos\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mdic_palavra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpalavra\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpolaridade\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "dic_palavra = {}\n",
    "for i in oplexicon.readlines():\n",
    "    pos_ponto = i.find(',')\n",
    "    palavra = (i[:pos_ponto])\n",
    "    pol_pos = re.findall(r'-?\\d+\\.?\\d*', str(i))\n",
    "    polaridade = pol_pos[0]\n",
    "    dic_palavra[palavra] = polaridade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Score_sentimento(frase):\n",
    "    frase = frase.lower()\n",
    "    l_sentimento = []\n",
    "    for p in frase.split():\n",
    "        l_sentimento.append(int(dic_palavra.get(p, 0)))\n",
    "    score = sum(l_sentimento)\n",
    "    if score > 0:\n",
    "        return 'Positivo, Score:{}'.format(score)\n",
    "    elif score == 0:\n",
    "        return 'Neutro, Score:{}'.format(score)\n",
    "    else:\n",
    "        return 'Negativo, Score:{}'.format(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = nltk.stem.RSLPStemmer()\n",
    "def Score_sentimento_Stemming(frase):\n",
    "    frase = frase.lower()\n",
    "    l_sentimento = []\n",
    "    for p in frase.split():\n",
    "        l_sentimento.append(int(dic_palavra.get(stemmer.stem(p), 0)))\n",
    "    score = sum(l_sentimento)\n",
    "    if score > 0:\n",
    "        return 'Positivo, Score:{}'.format(score)\n",
    "    elif score == 0:\n",
    "        return 'Neutro, Score:{}'.format(score)\n",
    "    else:\n",
    "        return 'Negativo, Score:{}'.format(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dInfoMoney = 'C:/Users/vitor/Documents/GetDataset/Infomoney/'\n",
    "dInvesting = 'C:/Users/vitor/Documents/GetDataset/Investing.com/'\n",
    "dTrading = 'C:/Users/vitor/Documents/GetDataset/TradingView/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# INFOMONEY\n",
    "with open(dInfoMoney + today +'/dataset.csv', encoding=\"utf8\") as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    next(reader)\n",
    "    dataInfomoney = [r for r in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(dInfoMoney + today +'/polarityOpLexicon.csv', mode='w', encoding=\"utf8\") as employee_file:\n",
    "    employee_writer = csv.writer(employee_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)    \n",
    "    for lista2 in dataInfomoney:\n",
    "        try:\n",
    "            x = Score_sentimento(lista2[2])\n",
    "            x = str(x)\n",
    "            employee_writer.writerow([lista2[2], x])\n",
    "        except IndexError:\n",
    "            x = 'null'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(dInfoMoney + today +'/polarityOpLexiconStemming.csv', mode='w', encoding=\"utf8\") as employee_file:\n",
    "    employee_writer = csv.writer(employee_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)    \n",
    "    for lista2 in dataInfomoney:\n",
    "        try:\n",
    "            x = Score_sentimento_Stemming(lista2[2])\n",
    "            x = str(x)\n",
    "            employee_writer.writerow([lista2[2], x])\n",
    "        except IndexError:\n",
    "            x = 'null'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# INVESTING.COM\n",
    "with open(dInvesting + today +'/dataset.csv', encoding=\"utf8\") as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    next(reader)\n",
    "    dataInvesting = [r for r in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(dInvesting + today +'/polarityOpLexicon.csv', mode='w', encoding=\"utf8\") as employee_file:\n",
    "    employee_writer = csv.writer(employee_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)    \n",
    "    for lista1 in dataInvesting:\n",
    "        try:\n",
    "            x = Score_sentimento(lista1[2])\n",
    "            x = str(x)\n",
    "            employee_writer.writerow([lista1[2], x])\n",
    "        except IndexError:\n",
    "            x = 'null'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(dInvesting + today +'/polarityOpLexiconStemming.csv', mode='w', encoding=\"utf8\") as employee_file:\n",
    "    employee_writer = csv.writer(employee_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)    \n",
    "    for lista1 in dataInvesting:\n",
    "        try:\n",
    "            x = Score_sentimento_Stemming(lista1[2])\n",
    "            x = str(x)\n",
    "            employee_writer.writerow([lista1[2], x])\n",
    "        except IndexError:\n",
    "            x = 'null'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- TRADING VIEW --------------------------------------------------------------------------------------------------\n",
    "with open(dTrading + today +'/dataset.csv', encoding=\"utf8\") as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    next(reader)\n",
    "    dataTrading = [r for r in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(dTrading + today +'/polarityOpLexicon.csv', mode='w', encoding=\"utf8\") as employee_file:\n",
    "    employee_writer = csv.writer(employee_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)    \n",
    "    for lista in dataTrading:\n",
    "        try:\n",
    "            x = Score_sentimento(lista[2])\n",
    "            x = str(x)\n",
    "            employee_writer.writerow([lista[2], x])\n",
    "        except IndexError:\n",
    "            x = 'null'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(dTrading + today +'/polarityOpLexiconStemming.csv', mode='w', encoding=\"utf8\") as employee_file:\n",
    "    employee_writer = csv.writer(employee_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)    \n",
    "    for lista in dataTrading:\n",
    "        try:\n",
    "            x = Score_sentimento_Stemming(lista[2])\n",
    "            x = str(x)\n",
    "            employee_writer.writerow([lista[2], x])\n",
    "        except IndexError:\n",
    "            x = 'null'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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