{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from document_polluter import DocumentPolluter\n",
    "import yaml\n",
    "import os\n",
    "import requests\n",
    "import json\n",
    "from collections import defaultdict\n",
    "from scipy import stats\n",
    "\n",
    "with open('credentials.yaml') as file:\n",
    "    credentials = yaml.load(file, Loader=yaml.FullLoader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('paragraphs/manual_gendered.yaml') as file:\n",
    "    documents = yaml.load(file, Loader=yaml.FullLoader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_google_sentiment(document):\n",
    "    url = f\"https://language.googleapis.com/v1/documents:analyzeSentiment?key={credentials['google']['key']}\"\n",
    "    headers = {'content-type': 'application/json'}\n",
    "    data = {\n",
    "      'document': {\n",
    "        'type': 'PLAIN_TEXT',\n",
    "        'content': document\n",
    "      }\n",
    "    }\n",
    "\n",
    "    r = requests.post(url=url, data=json.dumps(data), headers=headers)\n",
    "    return json.loads(r.text)['documentSentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment = defaultdict(list)\n",
    "for genre, docs in documents.items():\n",
    "    for document in docs:\n",
    "        sentiment[genre].append(get_google_sentiment(document))\n",
    "\n",
    "female_scores = [x['score'] for x in sentiment['female']]\n",
    "male_scores = [x['score'] for x in sentiment['male']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=20.500, p=0.322\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.mannwhitneyu(female_scores, male_scores)\n",
    "print('Statistics=%.3f, p=%.3f' % (stat, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for idx, document in enumerate(documents):\n",
    "    results.append({\n",
    "        'female_sentence': documents['female'][idx],\n",
    "        'male_sentence': documents['male'][idx],\n",
    "        'female_score': sentiment['female'][idx]['score'],\n",
    "        'male_score': sentiment['male'][idx]['score'],\n",
    "        'difference': abs(sentiment['female'][idx]['score'] - sentiment['male'][idx]['score'])\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(filter(lambda x: x['difference'] != 0, results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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