user_lists_for_knn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "498 users\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import time\n",
    "import tweepy\n",
    "import pandas as pd\n",
    "import nltk\n",
    "import numpy as np\n",
    "from tweepy import OAuthHandler\n",
    "from tweepy import Cursor\n",
    "from tweepy import Paginator\n",
    "\n",
    "#creazione dizionario dei brands\n",
    "brands_dict = {}\n",
    "brands_dict[\"alfaromeo\"] = 'AlfaRomeoUSA'\n",
    "brands_dict[\"samsung\"] = 'SamsungUS'\n",
    "brands_dict[\"lavazza\"] = 'lavazzaUSA'\n",
    "brands_dict[\"armani\"] = 'armani'\n",
    "brands_dict[\"adidas\"] = 'adidasUS'\n",
    "brands_dict[\"microsoft\"] = 'Microsoft'\n",
    "brands_dict[\"barilla\"] = 'BarillaUS'\n",
    "brands_dict[\"ferrero\"] = 'FerreroUK'\n",
    "brands_dict[\"delonghi\"] = 'DeLonghiUK'\n",
    "brands_dict[\"amarelli\"] = 'MuseoAmarelli'\n",
    "all_brands_list = ['alfaromeo', 'samsung', 'lavazza', 'armani', 'adidas', 'microsoft', 'barilla', 'ferrero', 'delonghi','amarelli']\n",
    "\n",
    "#CHIAVI API TWITTER \n",
    "cons_key = '7a1egZomKUUFJZd0fsLZTofTq'\n",
    "cons_secret = 'iPlTUWVDi3A6tt2BF6D0tDuYPjJ6yUzaMEzXEAw6dpkoSsXD1o'\n",
    "acc_token = '1497898238795472900-oqEVmk9x5UuMX61KzxTj5Jay6Rn4bV'\n",
    "acc_secret = 'xbGTCGvpQu8yl0M7c1qSWi5RY1w9rV2wMQzb2UrqhRCwP'\n",
    "bearer = 'AAAAAAAAAAAAAAAAAAAAAEcpjAEAAAAA8sTuDav%2BYe6qPyGGOjB6cv%2FtUbE%3DkqVbW8FVTHYSiXsiobUfgm9mU9EkLC8lbPv6El606gD3lfYOXa'\n",
    "client_server = '5xi46p9-pr7x0IjnAWU_dxb2Bz_UJNPo3PUfDMvfGEN4NP8nHG'\n",
    "\n",
    "#Funzione che restituisce l'autenticazione a Twitter\n",
    "def get_twitter_auth():\n",
    "    try:\n",
    "        consumer_key = cons_key\n",
    "        consumer_secret = cons_secret\n",
    "        access_token = acc_token\n",
    "        access_secret = acc_secret\n",
    "    except KeyError:\n",
    "        sys.stderr.write(\"Twitter Environment Variable not Set\\n\")\n",
    "        sys.exit(1)     \n",
    "    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "    auth.set_access_token(access_token, access_secret)\n",
    "    return auth\n",
    "\n",
    "\n",
    "#Funzione che restituisce il client per accedere all'API di autenticazione\n",
    "def get_twitter_client():\n",
    "    auth = get_twitter_auth()\n",
    "    client = tweepy.API(auth, wait_on_rate_limit=True) \n",
    "    return client\n",
    "\n",
    "\n",
    "#controlla se l'utente segue la pagina:\n",
    "#restituisce un booleano\n",
    "client = get_twitter_client()\n",
    "def check_friendship(source_screen_name, target_screen_name):\n",
    "    try:\n",
    "        friendship = client.get_friendship(source_screen_name = source_screen_name, target_screen_name = target_screen_name)\n",
    "        return friendship[0].following\n",
    "    except:\n",
    "        return False\n",
    "\n",
    "\n",
    "\n",
    "#lista degli utenti USERS-TAGS-VALUES\n",
    "users_tags_df = pd.read_csv('final_results/users_tags_values.csv')\n",
    "all_users_list = list(np.unique(users_tags_df[\"User\"])) #lista di utenti #lista di utenti\n",
    "print(str(len(all_users_list))+\" users\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GOLD STANDARD \n",
    "\n",
    "#triade di valori <User , Brand , boolean>, sul quale per coppia (Utente,Brand) attribuiamo\n",
    "# il parametro boolean per indicare se l'utente segue o meno la pagina ufficiale del brand.\n",
    "gold_standard = pd.DataFrame(columns=['User','Brand','boolean'])\n",
    "\n",
    "#riempimento del dataframe con i valori riportati da check_friendship\n",
    "#per tutti gli utenti\n",
    "for user in all_users_list:\n",
    "    print(\"Check for \"+user+\":\")\n",
    "    #per tutte le pagine dei brands\n",
    "    for brand in all_brands_list:\n",
    "        brand_page = brands_dict.get(brand)\n",
    "        #se l'utente segue la pagina:\n",
    "        if check_friendship(user,brand_page):\n",
    "            #assegna True\n",
    "            gold_standard.loc[len(gold_standard)] = [user,brand,True]\n",
    "        #se l'utente NON segue la pagina:\n",
    "        else:\n",
    "            #assegna False\n",
    "            gold_standard.loc[len(gold_standard)] = [user,brand,False]\n",
    "\n",
    "#salvataggio:\n",
    "if not os.path.exists('final_results'):\n",
    "    os.makedirs('final_results')\n",
    "gold_standard.to_csv(\"final_results/gold_standard_df.csv\", index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>User</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21Phylk</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABO_mondo</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACE2276</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGnomblea</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>wittystwitty</td>\n",
       "      <td>armani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>wittystwitty</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>wittystwitty</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>xkj1985x</td>\n",
       "      <td>samsung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>623 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             User      Brand\n",
       "0        007maybe    ferrero\n",
       "1         21Phylk    ferrero\n",
       "2       ABO_mondo   amarelli\n",
       "3         ACE2276  alfaromeo\n",
       "4       AGnomblea    ferrero\n",
       "..            ...        ...\n",
       "618  wittystwitty     armani\n",
       "619  wittystwitty  microsoft\n",
       "620  wittystwitty    ferrero\n",
       "621      xkj1985x    samsung\n",
       "622  ziopaolo2015   amarelli\n",
       "\n",
       "[623 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TRUE GOLD STANDARD: Lista degli elementi del GOLD STANDARD per i soli utenti con boolean True\n",
    "\n",
    "true_gold_standard = pd.read_csv('final_results/gold_standard_df.csv')\n",
    "true_gold_standard.drop(true_gold_standard[true_gold_standard.boolean == False].index, inplace=True)\n",
    "true_gold_standard = true_gold_standard.drop('boolean',axis=1).reset_index(drop=True)\n",
    "true_gold_standard.to_excel('final_results/true_gold_standard.xlsx',index=False)\n",
    "true_gold_standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>User</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AnteaterButters</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AshleyUGCsocial</td>\n",
       "      <td>lavazza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CharliCass</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CoriglianoCala1</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dawnr1979</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DeniseMorbin</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DigitalVTajc</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DitchMariapaola</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DuranWebDev</td>\n",
       "      <td>lavazza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FirstMediaTeam</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FirstMediaTeam</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FujiFreak</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GelateriaRomana</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HGHYGuangzhou</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>JaddenSmiith</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kre8tiveWitch</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Lina82333204</td>\n",
       "      <td>lavazza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MNVancouver</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MadeinItaly_App</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MariangelaBisco</td>\n",
       "      <td>armani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MariangelaBisco</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MarkPerozziello</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MarkPerozziello</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MatteoVolpePai</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MikeySSanchez</td>\n",
       "      <td>samsung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MikeySSanchez</td>\n",
       "      <td>microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MuseoArpaSalvi</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PrecisionProco</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RanieriAgency</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SavinoSteph</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SelectedEyewear</td>\n",
       "      <td>armani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SoluzMuseali</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>YleniaNea</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>annalolanardi</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>cakeandbakesuk</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>cutelilpuppytv</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>donnamarie2607</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>eghj4</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>elizmase</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>emsky2121</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>espressofied</td>\n",
       "      <td>lavazza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>fondpirelli</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>fondpirelli</td>\n",
       "      <td>armani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>joseser49285003</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>kaffeezentrale</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>lamentapiperit</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>liquoriceheaven</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>luxurytravel</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>molly1752</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>rosseaglesham</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>rudirob</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>sajjadm24811749</td>\n",
       "      <td>lavazza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>shnozzsauges1</td>\n",
       "      <td>delonghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>theramalmedia</td>\n",
       "      <td>samsung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>thesweetpaisana</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>tuscanysusie</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>weetish</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               User      Brand\n",
       "0   AnteaterButters    barilla\n",
       "1   AshleyUGCsocial    lavazza\n",
       "2        CharliCass    ferrero\n",
       "3   CoriglianoCala1   amarelli\n",
       "4         Dawnr1979   delonghi\n",
       "5      DeniseMorbin   delonghi\n",
       "6      DigitalVTajc  microsoft\n",
       "7   DitchMariapaola   delonghi\n",
       "8       DuranWebDev    lavazza\n",
       "9    FirstMediaTeam  microsoft\n",
       "10   FirstMediaTeam    ferrero\n",
       "11        FujiFreak   delonghi\n",
       "12  GelateriaRomana   amarelli\n",
       "13    HGHYGuangzhou  microsoft\n",
       "14     JaddenSmiith  alfaromeo\n",
       "15    Kre8tiveWitch   delonghi\n",
       "16     Lina82333204    lavazza\n",
       "17      MNVancouver  alfaromeo\n",
       "18  MadeinItaly_App   amarelli\n",
       "19  MariangelaBisco     armani\n",
       "20  MariangelaBisco   amarelli\n",
       "21  MarkPerozziello  alfaromeo\n",
       "22  MarkPerozziello  microsoft\n",
       "23   MatteoVolpePai  alfaromeo\n",
       "24    MikeySSanchez    samsung\n",
       "25    MikeySSanchez  microsoft\n",
       "26   MuseoArpaSalvi   amarelli\n",
       "27   PrecisionProco    ferrero\n",
       "28    RanieriAgency   delonghi\n",
       "29      SavinoSteph     adidas\n",
       "30  SelectedEyewear     armani\n",
       "31     SoluzMuseali   amarelli\n",
       "32        YleniaNea   amarelli\n",
       "33    annalolanardi   amarelli\n",
       "34   cakeandbakesuk    ferrero\n",
       "35   cutelilpuppytv    barilla\n",
       "36   donnamarie2607   delonghi\n",
       "37            eghj4   delonghi\n",
       "38         elizmase   delonghi\n",
       "39        emsky2121    ferrero\n",
       "40     espressofied    lavazza\n",
       "41      fondpirelli  alfaromeo\n",
       "42      fondpirelli     armani\n",
       "43  joseser49285003     adidas\n",
       "44   kaffeezentrale   amarelli\n",
       "45   lamentapiperit   amarelli\n",
       "46  liquoriceheaven   amarelli\n",
       "47     luxurytravel  alfaromeo\n",
       "48        molly1752   delonghi\n",
       "49    rosseaglesham   delonghi\n",
       "50          rudirob     adidas\n",
       "51  sajjadm24811749    lavazza\n",
       "52    shnozzsauges1   delonghi\n",
       "53    theramalmedia    samsung\n",
       "54  thesweetpaisana    barilla\n",
       "55     tuscanysusie    barilla\n",
       "56          weetish    ferrero"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CREAZIONE DELLO STRONG_GOLD STANDARD:\n",
    "# (lista di utenti presenti sul true_gold_standard) INTERSEZIONE (lista di utenti in cui la sentiment analysis ha dato un giudizio positivo per i brand)\n",
    "\n",
    "true_gold_standard = pd.read_excel('final_results/true_gold_standard.xlsx') #true_gold_standard\n",
    "true_users_list = np.unique(true_gold_standard['User'].to_list())           #lista degli utenti in true_gold_standard\n",
    "true_GS_dict = dict(tuple(true_gold_standard.groupby('User')))              #raggruppa gli utenti del true gold standard per User\n",
    "\n",
    "users_brands_df = pd.read_csv('final_results/users_brands_values.csv')      #tabella USERS-BRANDS VALUES\n",
    "\n",
    "\n",
    "def get_user_class(user):\n",
    "    return true_GS_dict.get(user)['Brand'].to_list()\n",
    "\n",
    "#restituisce il sentiment di un utente rispetto al brand\n",
    "def get_user_brand_sentiment(user,brand):\n",
    "    return users_brands_df.loc[users_brands_df['User'] == user,brand].values[0]\n",
    "\n",
    "#creazione del dataframe per strong_gold_standard\n",
    "strong_gold_standard = pd.DataFrame(columns=['User','Brand'])\n",
    "for user in true_users_list:\n",
    "    classes = get_user_class(user)\n",
    "    for brand in classes:\n",
    "        if get_user_brand_sentiment(user,brand) >0.67:\n",
    "            strong_gold_standard.loc[len(strong_gold_standard)] = [user,brand]\n",
    "\n",
    "strong_users_list = strong_gold_standard['User'].to_list()\n",
    "strong_gold_standard.to_excel('final_results/strong_gold_standard.xlsx',index=False)\n",
    "strong_gold_standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>User</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21Phylk</td>\n",
       "      <td>armani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21Phylk</td>\n",
       "      <td>adidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABO_mondo</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABO_mondo</td>\n",
       "      <td>armani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>thesweetpaisana</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>tuscanysusie</td>\n",
       "      <td>alfaromeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>tuscanysusie</td>\n",
       "      <td>barilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>weetish</td>\n",
       "      <td>ferrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>weetish</td>\n",
       "      <td>amarelli</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>331 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                User      Brand\n",
       "0           007maybe  alfaromeo\n",
       "1            21Phylk     armani\n",
       "2            21Phylk     adidas\n",
       "3          ABO_mondo  alfaromeo\n",
       "4          ABO_mondo     armani\n",
       "..               ...        ...\n",
       "326  thesweetpaisana    ferrero\n",
       "327     tuscanysusie  alfaromeo\n",
       "328     tuscanysusie    barilla\n",
       "329          weetish    ferrero\n",
       "330          weetish   amarelli\n",
       "\n",
       "[331 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CREAZIONE DELLO SENTIMENT_GOLD STANDARD:\n",
    "#lista di utenti in cui la sentiment analysis ha dato un giudizio positivo per i brand\n",
    "users_brands_df = pd.read_csv('final_results/users_brands_values.csv')  #tabella USERS-BRANDS VALUES\n",
    "all_users_list = list(np.unique(users_brands_df[\"User\"]))               #lista di utenti\n",
    "all_brands_list = ['alfaromeo', 'samsung', 'lavazza', 'armani', 'adidas', 'microsoft', 'barilla', 'ferrero', 'delonghi','amarelli']\n",
    "\n",
    "#restituisce il sentiment di un utente rispetto al brand\n",
    "def get_user_brand_sentiment(user,brand):\n",
    "    return users_brands_df.loc[users_brands_df['User'] == user,brand].values[0]\n",
    "\n",
    "#creazione del dataframe sentiment_gold_standard\n",
    "sentiment_gold_standard = pd.DataFrame(columns=['User','Brand'])\n",
    "for user in all_users_list:\n",
    "    for brand in all_brands_list:\n",
    "        if get_user_brand_sentiment(user,brand) >0.67:\n",
    "            sentiment_gold_standard.loc[len(sentiment_gold_standard)] = [user,brand]\n",
    "\n",
    "\n",
    "sentiment_gold_standard.to_excel('final_results/sentiment_gold_standard.xlsx',index=False)\n",
    "sentiment_gold_standard"
   ]
  }
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