Positive-negative analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import paralleldots\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data_scraping script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scraping data from the scites can be put here e.g. twitter.py\n",
    "#after scraping the data, it needs to be cleaned so that it can be fed into paralleldots api\n",
    "#like the facebook json facebook post data needs to be filtered out to get the required texts to be put into api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### eg. for facebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename= 'your_posts.json' #json file containg facebook posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(filename,'r') as j:\n",
    "    data = json.load(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = []\n",
    "for i in data.get('status_updates'):#filtering data to get posts\n",
    "\tif i.get('data') is not None:\n",
    "\t\ta.append(i.get('data'))\n",
    "b = []#list containg the facebook posts\n",
    "for i in a:\n",
    "\tb.append(i[0].get('post'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### paralleldots api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#three apis which can be bascially use is emotion, sentiment, abuse\n",
    "\n",
    "list0fFunctions = [paralleldots.sentiment,paralleldots.emotion, paralleldots.abuse]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in li:\n",
    "#evaluating each functional apis\n",
    "    for i in b[0:51]:#evaluating for list of first 50 values\n",
    "        di = j(i)\n",
    "        new[i] = di\n",
    "    for i in new:\n",
    "        #writing the values in csv files in the format function.csv\n",
    "        with open('{}.csv'.format((\"{}\".format(j)).split(\" \")[1][4:]), 'a') as csvfile:\n",
    "            writer = csv.writer(csvfile, delimiter = ',')\n",
    "            writer.writerow([i,new.get(i)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### laoding the csv output files for the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#basically what can be done is dividing the all the positive output into one model and negative output into one\n",
    "#then get the percentage of all his positive behaviour and negative behaviour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "sentiment = pd.read_csv('sentiment.csv')\n",
    "abuse = pd.read_csv('abuse.csv')\n",
    "emotion = pd.read_csv('emotion.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cleaning the datas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### first sentiment.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use this conditions of three types because of the different tyoe of outputs recieved from paralleldots api\n",
    "\n",
    "result = [] #list containg the end values\n",
    "for i in sentiment['value']:\n",
    "    if 'sentiment' in i.split(',')[0]:\n",
    "        result.append(i.split(',')[0].split(': ')[1].rstrip('\\'').lstrip('\\''))\n",
    "    elif 'sentiment' in (i.split(',')[3]):\n",
    "        result.append(i.split(',')[3].split(': ')[1].rstrip('\\'').lstrip('\\''))\n",
    "    elif 'sentiment' in (i.split(',')[4]):\n",
    "        result.append(i.split(',')[4].split(': ')[1].rstrip('}').rstrip('\\'').lstrip('\\''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writing the outputs in the files\n",
    "\n",
    "for i in result:\n",
    "    if i == 'positive':\n",
    "        with open('positive.csv','a') as pos:\n",
    "            writer = csv.writer(pos, delimiter=',')\n",
    "            writer.writerow([i])\n",
    "    elif i == 'negative':\n",
    "        with open('negative.csv','a') as pos:\n",
    "            writer = csv.writer(pos, delimiter=',')\n",
    "            writer.writerow([i])\n",
    "            \n",
    "#excluding all the neutral values since of no use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### abuse.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use this conditions of three types because of the different tyoe of outputs recieved from paralleldots api\n",
    "\n",
    "result_abuse = []\n",
    "for i in abuse['value']:\n",
    "    if 'sentence' in i.split(',')[1]:\n",
    "        result_abuse.append(i.split(',')[1].split(': ')[1].rstrip('\\'').lstrip('\\''))\n",
    "    elif 'sentence' in i.split(',')[0]:\n",
    "        result_abuse.append(i.split(',')[0].split(': ')[1].rstrip('\\'').lstrip('\\''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writing the outputs in the files\n",
    "\n",
    "for i in result_abuse:\n",
    "    if i == 'Non Abusive':\n",
    "        with open('positive.csv','a') as pos:\n",
    "            writer = csv.writer(pos, delimiter=',')\n",
    "            writer.writerow([i])\n",
    "    elif i == 'Abusive':\n",
    "        with open('negative.csv','a') as pos:\n",
    "            writer = csv.writer(pos, delimiter=',')\n",
    "            writer.writerow([i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### emotion.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use this conditions of three types because of the different tyoe of outputs recieved from paralleldots api\n",
    "\n",
    "result_emotion = []\n",
    "for i in emotion['value']:\n",
    "    if len(i.split('probabilities')[0]) > 14:\n",
    "        result_emotion.append(i.split('probabilities')[0].split(': ')[2].rstrip('\\' ,\\'').lstrip('\\''))\n",
    "    elif len(i.split('probabilities')[0]) <= 14:\n",
    "        result_emotion.append(i.split('probabilities')[1].split('emotion')[1].split('code')[0].lstrip('\\': \\'').rstrip('\\' ,}\\''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writing the outputs in the files\n",
    "\n",
    "positive_words = ['Happy', 'Excited','Sarcasm']\n",
    "negative_words = ['Angry', 'Fear', 'Bored', 'Sad']\n",
    "\n",
    "for i in result_emotion:\n",
    "    if i in positive_words:\n",
    "        with open('positive.csv','a') as pos:\n",
    "            writer = csv.writer(pos, delimiter=',')\n",
    "            writer.writerow([i])\n",
    "    elif i in negative_words:\n",
    "        with open('negative.csv','a') as pos:\n",
    "            writer = csv.writer(pos, delimiter=',')\n",
    "            writer.writerow([i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### analysing the positive and negative files created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive = pd.read_csv('positive.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
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       "      <td>positive</td>\n",
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       "      <th>24</th>\n",
       "      <td>positive</td>\n",
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       "      <th>25</th>\n",
       "      <td>positive</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>positive</td>\n",
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       "      <th>27</th>\n",
       "      <td>positive</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>positive</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>positive</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>80</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Non Abusive</td>\n",
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       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Non Abusive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        positive\n",
       "0       positive\n",
       "1       positive\n",
       "2       positive\n",
       "3       positive\n",
       "4       positive\n",
       "5       positive\n",
       "6       positive\n",
       "7       positive\n",
       "8       positive\n",
       "9       positive\n",
       "10      positive\n",
       "11      positive\n",
       "12      positive\n",
       "13      positive\n",
       "14      positive\n",
       "15      positive\n",
       "16      positive\n",
       "17      positive\n",
       "18      positive\n",
       "19      positive\n",
       "20      positive\n",
       "21      positive\n",
       "22      positive\n",
       "23      positive\n",
       "24      positive\n",
       "25      positive\n",
       "26      positive\n",
       "27      positive\n",
       "28      positive\n",
       "29      positive\n",
       "..           ...\n",
       "80   Non Abusive\n",
       "81   Non Abusive\n",
       "82   Non Abusive\n",
       "83   Non Abusive\n",
       "84   Non Abusive\n",
       "85   Non Abusive\n",
       "86   Non Abusive\n",
       "87   Non Abusive\n",
       "88   Non Abusive\n",
       "89   Non Abusive\n",
       "90   Non Abusive\n",
       "91   Non Abusive\n",
       "92   Non Abusive\n",
       "93   Non Abusive\n",
       "94   Non Abusive\n",
       "95   Non Abusive\n",
       "96   Non Abusive\n",
       "97   Non Abusive\n",
       "98   Non Abusive\n",
       "99   Non Abusive\n",
       "100  Non Abusive\n",
       "101  Non Abusive\n",
       "102  Non Abusive\n",
       "103  Non Abusive\n",
       "104  Non Abusive\n",
       "105  Non Abusive\n",
       "106  Non Abusive\n",
       "107  Non Abusive\n",
       "108  Non Abusive\n",
       "109  Non Abusive\n",
       "\n",
       "[110 rows x 1 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative = pd.read_csv('negative.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Bored</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Abusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Abusive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    negative\n",
       "0   negative\n",
       "1   negative\n",
       "2   negative\n",
       "3   negative\n",
       "4   negative\n",
       "5   negative\n",
       "6   negative\n",
       "7   negative\n",
       "8   negative\n",
       "9   negative\n",
       "10  negative\n",
       "11  negative\n",
       "12  negative\n",
       "13  negative\n",
       "14  negative\n",
       "15  negative\n",
       "16  negative\n",
       "17  negative\n",
       "18  negative\n",
       "19  negative\n",
       "20  negative\n",
       "21  negative\n",
       "22  negative\n",
       "23     Angry\n",
       "24     Angry\n",
       "25     Angry\n",
       "26      Fear\n",
       "27     Bored\n",
       "28     Angry\n",
       "29       Sad\n",
       "30     Angry\n",
       "31     Angry\n",
       "32     Angry\n",
       "33      Fear\n",
       "34   Abusive\n",
       "35   Abusive"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = len(positive)\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg = len(negative)\n",
    "neg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = pos + neg\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.34"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_percent = round(pos/total * 100,2)\n",
    "positive_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.66"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_percent = round(neg/total * 100,2)\n",
    "negative_percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result of the analysis the given data is 75.34% positive and 24.66% negative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get the counts of each word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(positive['positive'] == 'Happy').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(positive['positive'] == 'Sarcasm').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(negative['negative']=='Abusive').sum()"
   ]
  }
 ],
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