ResultLiwc.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 datetime\n",
    "now = datetime.datetime.now()\n",
    "today = now.strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultados TradingView\n",
    "r_tradingview = open(dTrading + today +'/polarityLiwc.csv', 'r', encoding='utf8')\n",
    "count = 0; posTrd = 0; neuTrd = 0; negTrd = 0\n",
    "for t in r_tradingview.readlines():\n",
    "    count += 1\n",
    "    if 'Positivo' in t:\n",
    "        posTrd += 1\n",
    "    if 'Neutro' in t:\n",
    "        neuTrd += 1\n",
    "    if 'Negativo' in t:\n",
    "        negTrd += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultados TradingView\n",
    "r_tradingview = open(dTrading + today +'/polarityLiwcStemming.csv', 'r', encoding='utf8')\n",
    "count = 0; pos = 0; neu = 0; neg = 0\n",
    "for t in r_tradingview.readlines():\n",
    "    count += 1\n",
    "    if 'Positivo' in t:\n",
    "        pos += 1\n",
    "    if 'Neutro' in t:\n",
    "        neu += 1\n",
    "    if 'Negativo' in t:\n",
    "        neg += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultados InfoMoney\n",
    "r_infomoney = open(dInfoMoney + today +'/polarityLiwc.csv', 'r', encoding='utf8')\n",
    "count = 0; posInf = 0; neuInf = 0; negInf = 0\n",
    "for t in r_infomoney.readlines():\n",
    "    count += 1\n",
    "    if 'Positivo' in t:\n",
    "        posInf += 1\n",
    "    if 'Neutro' in t:\n",
    "        neuInf += 1\n",
    "    if 'Negativo' in t:\n",
    "        negInf += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultados InfoMoney\n",
    "r_infomoney = open(dInfoMoney + today +'/polarityLiwcStemming.csv', 'r', encoding='utf8')\n",
    "count = 0; pos = 0; neu = 0; neg = 0\n",
    "for t in r_infomoney.readlines():\n",
    "    count += 1\n",
    "    if 'Positivo' in t:\n",
    "        pos += 1\n",
    "    if 'Neutro' in t:\n",
    "        neu += 1\n",
    "    if 'Negativo' in t:\n",
    "        neg += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultados Investing.com\n",
    "r_investing = open(dInvesting + today +'/polarityLiwc.csv', 'r', encoding='utf8')\n",
    "count = 0; posInv = 0; neuInv = 0; negInv = 0\n",
    "for t in r_investing.readlines():\n",
    "    count += 1\n",
    "    if 'Positivo' in t:\n",
    "        posInv += 1\n",
    "    if 'Neutro' in t:\n",
    "        neuInv += 1\n",
    "    if 'Negativo' in t:\n",
    "        negInv += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultados Investing.com\n",
    "r_investing = open(dInvesting + today +'/polarityLiwcStemming.csv', 'r', encoding='utf8')\n",
    "count = 0; pos = 0; neu = 0; neg = 0\n",
    "for t in r_investing.readlines():\n",
    "    count += 1\n",
    "    if 'Positivo' in t:\n",
    "        pos += 1\n",
    "    if 'Neutro' in t:\n",
    "        neu += 1\n",
    "    if 'Negativo' in t:\n",
    "        neg += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fonte de Dados</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Neu</th>\n",
       "      <th>Neg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Investing.com</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>InfoMoney</td>\n",
       "      <td>0</td>\n",
       "      <td>962</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TradingView</td>\n",
       "      <td>17</td>\n",
       "      <td>161</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Fonte de Dados  Pos  Neu  Neg\n",
       "0  Investing.com    0   61   38\n",
       "1      InfoMoney    0  962  345\n",
       "2    TradingView   17  161  450"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = {'Fonte de Dados': ['Investing.com', 'InfoMoney', 'TradingView'],\n",
    "        'Pos': [posInv, posInf, posTrd],\n",
    "        'Neu': [neuInv, neuInf, neuTrd],\n",
    "        'Neg': [negInv, negInf, negTrd]}\n",
    "df = pd.DataFrame(raw_data, columns = ['Fonte de Dados', 'Pos', 'Neu', 'Neg'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x241e9c417b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the positions and width for the bars\n",
    "pos = list(range(len(df['Pos']))) \n",
    "width = 0.25 \n",
    "    \n",
    "# Plotting the bars\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "\n",
    "# Create a bar with pre_score data,\n",
    "# in position pos,\n",
    "plt.bar(pos, \n",
    "        #using df['pre_score'] data,\n",
    "        df['Pos'], \n",
    "        # of width\n",
    "        width, \n",
    "        # with alpha 0.5\n",
    "        alpha=0.5, \n",
    "        # with color\n",
    "        color='#EE3224', \n",
    "        # with label the first value in first_name\n",
    "        label=df['Fonte de Dados'][0]) \n",
    "\n",
    "# Create a bar with mid_score data,\n",
    "# in position pos + some width buffer,\n",
    "plt.bar([p + width for p in pos], \n",
    "        #using df['mid_score'] data,\n",
    "        df['Neu'],\n",
    "        # of width\n",
    "        width, \n",
    "        # with alpha 0.5\n",
    "        alpha=0.5, \n",
    "        # with color\n",
    "        color='#F78F1E', \n",
    "        # with label the second value in first_name\n",
    "        label=df['Fonte de Dados'][1]) \n",
    "\n",
    "# Create a bar with post_score data,\n",
    "# in position pos + some width buffer,\n",
    "plt.bar([p + width*2 for p in pos], \n",
    "        #using df['post_score'] data,\n",
    "        df['Neg'], \n",
    "        # of width\n",
    "        width, \n",
    "        # with alpha 0.5\n",
    "        alpha=0.5, \n",
    "        # with color\n",
    "        color='#FFC222', \n",
    "        # with label the third value in first_name\n",
    "        label=df['Fonte de Dados'][2]) \n",
    "\n",
    "# Set the y axis label\n",
    "ax.set_ylabel('N° de Textos')\n",
    "\n",
    "# Set the position of the x ticks\n",
    "ax.set_xticks([p + 1 * width for p in pos])\n",
    "\n",
    "# Set the labels for the x ticks\n",
    "ax.set_xticklabels(df['Fonte de Dados'])\n",
    "\n",
    "# Setting the x-axis and y-axis limits\n",
    "plt.xlim(min(pos)-width, max(pos)+width*4)\n",
    "plt.ylim([0, max(df['Pos'] + df['Neu'] + df['Neg'])] )\n",
    "\n",
    "# Adding the legend and showing the plot\n",
    "plt.legend(['Positivo', 'Neutro', 'Negativo'], loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}