├── CarouselClass.py
├── OutputParser.py
├── README.md
├── Rinput
    ├── en.R
    ├── it.R
    ├── itastopwords.rda
    └── sent.csv
├── ScrapeAdvisor.py
├── ScraperClass.py
├── ipak.R
├── requirements.txt
└── res
    └── about.png


/CarouselClass.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | import time
  3 | import logging
  4 | 
  5 | from pathlib import Path
  6 | from kivy.app import App
  7 | from kivy.uix.carousel import Carousel
  8 | from kivy.uix.image import AsyncImage
  9 | from OutputParser import OutputParser
 10 | 
 11 | logging.basicConfig(level=logging.INFO)
 12 | logger = logging.getLogger(__name__)
 13 | 
 14 | 
 15 | # Carousel for sliding graphs
 16 | class CarouselApp(App):
 17 |     def __init__(self, **kwargs):
 18 |         super().__init__(**kwargs)
 19 |         self.lang = ""
 20 | 
 21 |     def set_lang(self, arg):
 22 |         self.lang = arg
 23 | 
 24 |     def build(self):
 25 |         # create the Routput directory if not exists
 26 |         try:
 27 |             os.mkdir("Routput")
 28 |         except OSError:
 29 |             logger.info("Routput directory already exists")
 30 |         else:
 31 |             logger.info("Successfully created the directory Routput")
 32 | 
 33 |         # create OutputParser object to parse analysis output
 34 |         output_parser = OutputParser()
 35 |         output_parser.parse()
 36 |         output_parser.save()
 37 |         output_parser.to_image()
 38 |         time.sleep(1)
 39 | 
 40 |         # create Carousel object
 41 |         carousel = Carousel(direction='right')
 42 | 
 43 |         # and add images from Routput
 44 |         try:
 45 |             if Path("Routput/stats.png").is_file():
 46 |                 image1 = AsyncImage(source="Routput/stats.png", nocache=True)
 47 |                 carousel.add_widget(image1)
 48 | 
 49 |             if Path("Routput/locations.png").is_file():
 50 |                 image2 = AsyncImage(source="Routput/locations.png", nocache=True)
 51 |                 carousel.add_widget(image2)
 52 | 
 53 |             if Path("Routput/neg_rev.png").is_file():
 54 |                 image3 = AsyncImage(source="Routput/neg_rev.png", nocache=True)
 55 |                 carousel.add_widget(image3)
 56 | 
 57 |             if Path("Routput/pos_rev.png").is_file():
 58 |                 image4 = AsyncImage(source="Routput/pos_rev.png", nocache=True)
 59 |                 carousel.add_widget(image4)
 60 | 
 61 |             # if Path("Routput/world_map.png").is_file() and os.path.getsize("Routput/world_map.png") > 3000:
 62 |             #   image5 = AsyncImage(source="Routput/world_map.png", nocache=True)
 63 |             #    carousel.add_widget(image5)
 64 | 
 65 |             if Path("Routput/rev_per_week.png").is_file():
 66 |                 image6 = AsyncImage(source="Routput/rev_per_week.png", nocache=True)
 67 |                 carousel.add_widget(image6)
 68 | 
 69 |             if Path("Routput/25_most_common_words.png").is_file():
 70 |                 image7 = AsyncImage(source="Routput/25_most_common_words.png", nocache=True)
 71 |                 carousel.add_widget(image7)
 72 | 
 73 |             if Path("Routput/25_wordclouds.png").is_file():
 74 |                 image8 = AsyncImage(source="Routput/25_wordclouds.png", nocache=True)
 75 |                 carousel.add_widget(image8)
 76 | 
 77 |             if Path("Routput/9_growing.png").is_file():
 78 |                 image9 = AsyncImage(source="Routput/9_growing.png", nocache=True)
 79 |                 carousel.add_widget(image9)
 80 | 
 81 |             if Path("Routput/9_decreasing.png").is_file():
 82 |                 image10 = AsyncImage(source="Routput/9_decreasing.png", nocache=True)
 83 |                 carousel.add_widget(image10)
 84 | 
 85 |             if Path("Routput/sent_afinn.png").is_file():
 86 |                 image11 = AsyncImage(source="Routput/sent_afinn.png", nocache=True)
 87 |                 carousel.add_widget(image11)
 88 | 
 89 |             # check if reviews are in Italian or English
 90 |             if output_parser.get_lang() == "en":
 91 |                 logger.info("[*] Reviews are in English")
 92 |                 if Path("Routput/sent_bing.png").is_file():
 93 |                     image12 = AsyncImage(source="Routput/sent_bing.png", nocache=True)
 94 |                     carousel.add_widget(image12)
 95 |             else:
 96 |                 logger.info("[*] Reviews are in Italian")
 97 |                 if Path("Routput/25_not_follows.png").is_file():
 98 |                     image12 = AsyncImage(source="Routput/25_not_follows.png", nocache=True)
 99 |                     carousel.add_widget(image12)
100 | 
101 |             if Path("Routput/not_follows.png").is_file():
102 |                 image13 = AsyncImage(source="Routput/not_follows.png", nocache=True)
103 |                 carousel.add_widget(image13)
104 | 
105 |         except Exception as e:
106 |             logger.error(e)
107 | 
108 |         return carousel
109 | 


--------------------------------------------------------------------------------
/OutputParser.py:
--------------------------------------------------------------------------------
  1 | import logging
  2 | import os
  3 | import re
  4 | import textwrap
  5 | 
  6 | from PIL import Image, ImageDraw, ImageFont
  7 | 
  8 | logging.basicConfig(level=logging.INFO)
  9 | logger = logging.getLogger(__name__)
 10 | 
 11 | 
 12 | # parse the output file made by R to retrieve information
 13 | class OutputParser:
 14 |     def __init__(self):
 15 |         self.diz = {}
 16 |         self.stats = ""
 17 |         self.pos_rev = ""
 18 |         self.neg_rev = ""
 19 |         self.lang = ""
 20 |         self.locations = ""
 21 | 
 22 |     def get_lang(self):
 23 |         return self.lang
 24 | 
 25 |     # parse the .txt file
 26 |     def parse(self):
 27 |         filepath = "Routput/out.txt"
 28 |         if os.path.isfile(filepath):
 29 |             try:
 30 |                 with open(filepath) as f:
 31 |                     line = f.readline()
 32 |                     cnt = 1
 33 |                     while line:
 34 |                         if (cnt == 1):
 35 |                             if "it" in line:
 36 |                                 self.lang = "it"
 37 |                             else:
 38 |                                 self.lang = "en"
 39 | 
 40 |                         if cnt == 2:
 41 |                             line = line.split()[1]
 42 |                             line = line.replace(" ", "")
 43 |                             self.diz["num_rev"] = line
 44 | 
 45 |                         if cnt == 3:
 46 |                             line = line.replace("[1]", "")
 47 |                             line = line.replace("\"", "")
 48 |                             line = line.replace(" ", "")
 49 |                             self.diz["from_date"] = line
 50 | 
 51 |                         if cnt == 4:
 52 |                             line = line.replace("[1]", "")
 53 |                             line = line.replace("\"", "")
 54 |                             line = line.replace(" ", "")
 55 |                             self.diz["to_date"] = line
 56 | 
 57 |                         if "bigram" in line:
 58 |                             lines = ""
 59 |                             for x in range(0, 11):
 60 |                                 line = f.readline().rstrip()
 61 |                                 if "<chr>" in line:
 62 |                                     lines = "\n"
 63 |                                     cnt += 1
 64 |                                 elif x == 10:
 65 |                                     lines += line[0:2] + "). " + line[3:-1].capitalize() + "(" + line[
 66 |                                         -1] + ")" + "\n"
 67 |                                     cnt += 1
 68 |                                 else:
 69 |                                     lines += "  " + line[1] + "). " + line[3:-1].capitalize() + "(" + line[
 70 |                                         -1] + ")" + "\n"
 71 |                                     cnt += 1
 72 |                             lines = lines.replace(" ", "", 1)  # removes only the first white space which occurs
 73 |                             lines = re.sub(' +', ' ', lines)
 74 |                             self.diz["bigram"] = lines
 75 | 
 76 |                         if "trigram" in line:
 77 |                             lines = ""
 78 |                             for x in range(0, 11):
 79 |                                 line = f.readline().rstrip()
 80 |                                 if "<chr>" in line:
 81 |                                     lines = "\n"
 82 |                                     cnt += 1
 83 |                                 elif x == 10:
 84 |                                     lines += line[0:2] + "). " + line[3:-1].capitalize() + "(" + line[
 85 |                                         -1] + ")" + "\n"
 86 |                                     cnt += 1
 87 |                                 else:
 88 |                                     lines += "  " + line[1] + "). " + line[3:-1].capitalize() + "(" + line[
 89 |                                         -1] + ")" + "\n"
 90 |                                     cnt += 1
 91 |                             lines = lines.replace(" ", "", 1)  # removes only the first white space which occurs
 92 |                             lines = re.sub(' +', ' ', lines)
 93 |                             self.diz["trigram"] = lines
 94 | 
 95 |                         if "pos_rev" in line:
 96 |                             for x in range(0, 1):
 97 |                                 line = f.readline().rstrip()
 98 |                                 line = line.replace("[1]", "")
 99 |                                 line = line.replace(" ", "", 1)
100 |                                 self.diz["pos_rev"] = line
101 | 
102 |                         if "neg_rev" in line:
103 |                             for x in range(0, 1):
104 |                                 line = f.readline().rstrip()
105 |                                 line = line.replace("[1]", "")
106 |                                 line = line.replace(" ", "", 1)
107 |                                 self.diz["neg_rev"] = line
108 | 
109 |                         if "cities" in line:
110 |                             lines = ""
111 |                             cnt += 1
112 |                             line = f.readline().rstrip()
113 |                             if "tibble" in line:
114 |                                 i = int(line.split()[3])
115 |                                 i += 3
116 |                                 for x in range(0, i):
117 |                                     line = f.readline().rstrip()
118 |                                     if "<chr>" in line:
119 |                                         lines = "\n"
120 |                                         cnt += 1
121 | 
122 |                                     elif "city" in line:
123 |                                         lines = "\n"
124 |                                         cnt += 1
125 | 
126 |                                     elif "Groups" in line:
127 |                                         lines = "\n"
128 |                                         cnt += 1
129 | 
130 |                                     elif "<NA>" in line:
131 |                                         line = line.replace("<", "")
132 |                                         line = line.replace(">", "")
133 |                                         splitted = line.split()
134 |                                         lines += 2 * " " + splitted[0] + "). " + str(
135 |                                             " ".join(splitted[1:-1])) + " (" + str(splitted[-1]) + ")" "\n"
136 |                                         cnt += 1
137 | 
138 |                                     elif x > 11:  # if number became 10, add only one space
139 |                                         splitted = line.split()
140 |                                         lines += " " + splitted[0] + "). " + str(
141 |                                             " ".join(splitted[1:-1])).capitalize() + " (" + str(splitted[-1]) + ")" "\n"
142 |                                         cnt += 1
143 | 
144 |                                     else:
145 |                                         splitted = line.split()
146 |                                         lines += 2 * " " + splitted[0] + "). " + str(
147 |                                             " ".join(splitted[1:-1])).capitalize() + " (" + str(splitted[-1]) + ")" "\n"
148 |                                         cnt += 1
149 | 
150 |                                 # lines = lines.replace(" ", "", 1)  # removes only the first white space which occurs
151 |                                 # lines = re.sub(' +', ' ', lines)
152 |                                 self.diz["cities"] = lines
153 | 
154 |                         if "states" in line:
155 |                             lines = ""
156 |                             cnt += 1
157 |                             line = f.readline().rstrip()
158 |                             if "tibble" in line:
159 |                                 i = int(line.split()[3])
160 |                                 i += 2
161 |                                 for x in range(0, i):
162 |                                     line = f.readline().rstrip()
163 |                                     if "<chr>" in line:
164 |                                         lines = "\n"
165 |                                         cnt += 1
166 | 
167 |                                     elif "state" in line:
168 |                                         lines = "\n"
169 |                                         cnt += 1
170 | 
171 |                                     elif "<NA>" in line:
172 |                                         line = line.replace("<", "")
173 |                                         line = line.replace(">", "")
174 |                                         splitted = line.split()
175 |                                         lines += 2 * " " + splitted[0] + "). " + str(
176 |                                             " ".join(splitted[1:-1])) + " (" + str(
177 |                                             splitted[-1]) + ")" "\n"
178 |                                         cnt += 1
179 | 
180 |                                     elif x > 11:  # if number became 10, add only one space
181 |                                         splitted = line.split()
182 |                                         lines += " " + splitted[0] + "). " + str(
183 |                                             " ".join(splitted[1:-1])).capitalize() + " (" + str(splitted[-1]) + ")" "\n"
184 |                                         cnt += 1
185 | 
186 |                                     else:
187 |                                         splitted = line.split()
188 |                                         lines += 2 * " " + splitted[0] + "). " + str(
189 |                                             " ".join(splitted[1:-1])).capitalize() + " (" + str(splitted[-1]) + ")" "\n"
190 |                                         cnt += 1
191 | 
192 |                                 # lines = lines.replace(" ", "", 1)  # removes only the first white space which occurs
193 |                                 # lines = re.sub(' +', ' ', lines)
194 |                                 self.diz["states"] = lines
195 | 
196 |                         line = f.readline().rstrip()
197 |                         cnt += 1
198 |                 f.close()
199 | 
200 |             except Exception as e:
201 |                 logger.error(e)
202 | 
203 |         else:
204 |             logger.error("[X]" + filepath + " doesn't exist")
205 | 
206 |     # save the text
207 |     def save(self):
208 |         for x, y in self.diz.items():
209 |             if x == "pos_rev":
210 |                 if self.lang == "en":
211 |                     self.pos_rev += ("Most positive review:\n" + "\n".join(textwrap.wrap(y, width=115)) + "\n\n")
212 |                 else:
213 |                     self.pos_rev += ("Recensione più positiva:\n" + "\n".join(textwrap.wrap(y, width=115)) + "\n\n")
214 | 
215 |             elif x == "neg_rev":
216 |                 if self.lang == "en":
217 |                     self.neg_rev += ("Most negative review:\n" + "\n".join(textwrap.wrap(y, width=115)) + "\n\n")
218 |                 else:
219 |                     self.neg_rev += ("Recensione più negativa:\n" + "\n".join(textwrap.wrap(y, width=115)) + "\n\n")
220 | 
221 |             elif x == "cities":
222 |                 if self.lang == "en":
223 |                     self.locations += ("Top 10 users' cities:" + y + "\n\n")
224 |                 else:
225 |                     self.locations += ("Le top 10 città di provenienza degli utenti:" + y + "\n\n")
226 | 
227 |             elif x == "states":
228 |                 if self.lang == "en":
229 |                     self.locations += ("Top 10 users' states:" + y + "\n\n")
230 |                 else:
231 |                     self.locations += ("I top 10 Stati di provenienza gli utenti:" + y + "\n\n")
232 | 
233 |             else:
234 |                 if x == "num_rev":
235 |                     if self.lang == "en":
236 |                         self.stats += ("Number of reviews: " + y + "\n\n")
237 |                     else:
238 |                         self.stats += ("Numero di recensioni: " + y + "\n\n")
239 | 
240 |                 elif x == "from_date":
241 |                     if self.lang == "en":
242 |                         self.stats += ("From: " + y + "\n\n")
243 |                     else:
244 |                         self.stats += ("Da: " + y + "\n\n")
245 | 
246 |                 elif x == "to_date":
247 |                     if self.lang == "en":
248 |                         self.stats += ("To: " + y + "\n\n")
249 |                     else:
250 |                         self.stats += ("A: " + y + "\n\n")
251 | 
252 |                 elif x == "bigram":
253 |                     if self.lang == "en":
254 |                         self.stats += ("Most common pair of consecutive words and number of occurrences: " + y + "\n\n")
255 |                     else:
256 |                         self.stats += (
257 |                                 "Le coppie più popolari di parole consecutive e numero di occorrenze:" + y + "\n\n")
258 | 
259 |                 elif x == "trigram":
260 |                     if self.lang == "en":
261 |                         self.stats += ("Most common trio of consecutive words and number of occurrences: " + y + "\n\n")
262 |                     else:
263 |                         self.stats += ("I trii più popolari di parole consecutive e numero di occorrenze:" + y + "\n\n")
264 | 
265 |     # put text into image
266 |     def to_image(self):
267 |         img1 = Image.new('RGB', (1920, 1080), color=(73, 109, 137))
268 |         img2 = Image.new('RGB', (1920, 1080), color=(73, 109, 137))
269 |         img3 = Image.new('RGB', (1920, 1080), color=(73, 109, 137))
270 |         img4 = Image.new('RGB', (1920, 1080), color=(73, 109, 137))
271 | 
272 |         font = ImageFont.truetype("C:\Windows\Fonts\Verdana.ttf", 30)
273 | 
274 |         d1 = ImageDraw.Draw(img1)
275 |         d1.text((50, 10), self.stats, fill=(255, 255, 0), font=font)
276 |         img1.save('Routput/stats.png')
277 | 
278 |         d2 = ImageDraw.Draw(img2)
279 |         d2.text((50, 50), self.pos_rev, fill=(255, 255, 0), font=font)
280 |         img2.save('Routput/pos_rev.png')
281 | 
282 |         d3 = ImageDraw.Draw(img3)
283 |         d3.text((50, 50), self.neg_rev, fill=(255, 255, 0), font=font)
284 |         img3.save('Routput/neg_rev.png')
285 | 
286 |         d4 = ImageDraw.Draw(img4)
287 |         d4.text((50, 50), self.locations, fill=(255, 255, 0), font=font)
288 |         img4.save('Routput/locations.png')
289 | 
290 | # Example
291 | # o = OutputParser()
292 | # o.parse()
293 | # o.save()
294 | # o.to_image()
295 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | <p align="center">
  2 |   <a href="" rel="noopener">
  3 |  <img width=200px height=200px src="https://github.com/blackeko/scrapeadvisor/blob/media/logo.png" alt="Project logo"></a>
  4 | </p>
  5 | 
  6 | <h3 align="center">scrapeadvisor</h3>
  7 | 
  8 | <div align="center">
  9 | 
 10 |   [![Status](https://img.shields.io/badge/status-active-success.svg)]() 
 11 |   [![License](https://img.shields.io/badge/license-GPL3-blue.svg)](/LICENSE)
 12 | 
 13 | </div>
 14 | 
 15 | ---
 16 | 
 17 | <p align="center">
 18 | 	A user-friendly python-based GUI which provides sentiment analysis of users' reviews toward a specific TripAdvisor facility 
 19 |     <br> 
 20 | </p>
 21 | 
 22 | ## Table of Contents
 23 | 
 24 | - [About](#about)
 25 | - [Getting Started](#getting_started)
 26 | - [Run](#run)
 27 | - [Usage](#usage)
 28 | - [Statistics](#statistics)
 29 | - [Supported Languages](#languages)
 30 | - [Built Using](#built_using)
 31 | - [Authors](#authors)
 32 | - [Acknowledgments](#acknowledgement)
 33 | - [Disclaimer](#disclaimer)
 34 | 
 35 | ## About <a name = "about"></a>
 36 | 
 37 | If you're reading, dear Tripadvisor, Inc., hire me!
 38 | 
 39 | ## Getting Started <a name = "getting_started"></a>
 40 | 
 41 | ### Prerequisites
 42 | 
 43 | - [Python](https://www.python.org/downloads/) installed 
 44 | - [R](https://cran.r-project.org/bin/windows/base/) installed 
 45 | 
 46 | ### Installing
 47 | 
 48 | Make sure you've all Python dependencies installed with:
 49 | 
 50 | ```console
 51 | scrape@advisor:~$ pip3 install -r requirements.txt
 52 | ```
 53 | 
 54 | Also, the following R packages are needed:
 55 | 
 56 | - dplyr 
 57 | - readr 
 58 | - lubridate
 59 | - ggplot2
 60 | - tidytext
 61 | - tidyverse 
 62 | - stringr
 63 | - tidyr
 64 | - scales
 65 | - broom 
 66 | - purrr
 67 | - widyr 
 68 | - igraph
 69 | - ggraph
 70 | - SnowballC
 71 | - wordcloud
 72 | - reshape2
 73 | - TeachingDemos
 74 | 
 75 | You can manually install missing ones with: 
 76 | 
 77 | ```R
 78 | install.packages("library_name")
 79 | ```
 80 | 
 81 | or run [this script](https://github.com/blackeko/scrapeadvisor/blob/master/ipak.R) (credit to [@stevenworthington](https://gist.github.com/stevenworthington)) to install them all.
 82 | 
 83 | ### Note
 84 | 
 85 | For Italian language support, **TextWiller** library must be installed.<br/>
 86 | To do that:
 87 | 
 88 | ```R
 89 | install.packages("devtools") 
 90 | install_github("livioivil/TextWiller")
 91 | ```
 92 | 
 93 | ## Run <a name = "run"></a>
 94 | 
 95 | In order to launch *scrapeadvisor* GUI, run:
 96 | 
 97 | ```console
 98 | scrape@advisor:~$ python3 ScrapeAdvisor.py
 99 | ```
100 | 
101 | ## Usage <a name="usage"></a>
102 | 
103 | ### Insert URL
104 | 
105 | 1. Insert the main page URL of a TripAdvisor structure (pub/restaurant/hotel/whatever) in the **URL bar** and click **Enter** (or press Enter)
106 | 2. Wait until **"Reviews Loaded"** label appears (may take time, depending on number of reviews)
107 | 
108 | ### Show Reviews
109 | 
110 | After the download is finished, press **"Show Reviews"** to see all the downloaded reviews.
111 | 
112 | ### Sentiment Analysis
113 | 
114 | After the download is finished, press **"Sentiment Analysis"** button and wait: all the graphs related to the facility will appear follow after, so you can **swipe** between them.
115 | 
116 | ## Statistics <a name="statistics"></a>
117 | 
118 | - Frequent **couple/trio of consecutive words** (bigrams/trigrams)
119 | - Most **positive/negative review**
120 | - Top **positive/negative sentiments** of users
121 | - The **trending/shrinking words** 
122 | - **Users' main cities**
123 | 
124 | ## Screenshot <a name="screenshot"></a>
125 | 
126 | <table style="width:100%">
127 | 		<tr>
128 | 			<td><img src="https://github.com/blackeko/scrapeadvisor/blob/media/word_cloud.png" ></td>
129 | 			<td><img src="https://github.com/blackeko/scrapeadvisor/blob/media/common_words.png" ></td>
130 | 		</tr>
131 | 		<tr>
132 | 			<td><img src="https://github.com/blackeko/scrapeadvisor/blob/media/sent_afinn.png" ></td>
133 | 			<td><img src="https://github.com/blackeko/scrapeadvisor/blob/media/shrinking.png" ></td>
134 | 		</tr>
135 | </table>
136 | 
137 | ## Supported Languages <a name="languages"></a>
138 | 
139 | - English
140 | - Italian
141 | 
142 | ## Built Using <a name = "built_using"></a>
143 | 
144 | - [Kivy](https://kivy.org/#home) - GUI
145 | - [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) - HTML scraping 
146 | - [R](https://www.r-project.org/about.html) - Sentiment Analysis
147 | 
148 | 
149 | ## Acknowledgements <a name = "acknowledgement"></a>
150 | 
151 | - [@susanli2016](https://github.com/susanli2016) - [Web Scraping TripAdvisor](https://towardsdatascience.com/scraping-tripadvisor-text-mining-and-sentiment-analysis-for-hotel-reviews-cc4e20aef333)
152 | - [TextWiller](https://github.com/livioivil/TextWiller) - For providing Italian stop words and lexicon 
153 | - All the other [packages](#about) - Thank you for being you
154 | 
155 | ## Disclaimer
156 | 
157 | *Scrapeadvisor* is provided under this License on an AS-IS basis, **without warranty of any kind**, either expressed, implied, or statutory, including, without limitation, warranties that the *scrapeadvisor* is free of defects, merchantable, fit for a particular purpose or non-infringing.
158 | 
159 | To the extent permitted under Law, *scrapeadvisor* is provided under an AS-IS basis. The *scrapeadvisor* Team shall never, and without any limit, be liable for any damage, cost, expense or any other payment incurred as a result of *scrapeadvisor*'s actions, failure, bugs and/or any other interaction between *scrapeadvisor* and end-equipment, computers, other software or any 3rd party, end-equipment, computer or services.
160 | 
161 | We **do not encourage** running *scrapeadvisor* against Tripadvisor without prior mutual consent. The *scrapeadvisor* Team accept no liability and are not responsible for any misuse or damage caused by *scrapeadvisor*.
162 | 


--------------------------------------------------------------------------------
/Rinput/en.R:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env Rscript
  2 | args = commandArgs(trailingOnly=TRUE)
  3 | args <- commandArgs(TRUE)
  4 | filename <- as.character(args[1])
  5 | library(dplyr)
  6 | library(readr)
  7 | library(lubridate)
  8 | library(ggplot2)
  9 | library(tidytext)
 10 | library(tidyverse)
 11 | library(stringr)
 12 | library(tidyr)
 13 | library(scales)
 14 | library(broom)
 15 | library(purrr)
 16 | library(widyr)
 17 | library(igraph)
 18 | library(ggraph)
 19 | library(SnowballC)
 20 | library(wordcloud)
 21 | library(reshape2)
 22 | library(TeachingDemos)
 23 | library(rworldmap)
 24 | theme_set(theme_minimal())
 25 | 
 26 | 
 27 | # min & max date
 28 | df <- read_csv(filename)
 29 | #df <- df[complete.cases(df), ] # remove row if one of the attr value is NA
 30 | Sys.setlocale("LC_TIME", "C")
 31 | df$review_date <- as.Date(df$review_date,format = "%B %d, %Y")
 32 | setwd("Routput")
 33 | txtStart("out.txt", commands=FALSE, results=TRUE, append=FALSE, visible.only=FALSE)
 34 | print("en")
 35 | dim(df); min(df$review_date); max(df$review_date)
 36 | txtStop()
 37 | 
 38 | # numbers rev per week
 39 | png("rev_per_week.png", width = 1920, height = 1080, res=300)
 40 | df %>%
 41 |   count(Week = round_date(review_date, "week")) %>%
 42 |   ggplot(aes(Week, n)) +
 43 |   geom_line() + 
 44 |   ggtitle('The Number of Reviews Per Week')
 45 |   dev.off()
 46 | 
 47 |   
 48 | # most common words
 49 | df <- tibble::rowid_to_column(df, "ID")
 50 | df <- df %>%
 51 |   mutate(review_date = as.POSIXct(review_date, origin = "1970-01-01"), month = round_date(review_date, "month"))
 52 | 
 53 | 
 54 | # locations
 55 | locations <- df %>%
 56 |   group_by(user_location) %>%
 57 |   summarise(count = length(user_location))
 58 | 
 59 | # cities and States
 60 | locations <- locations %>%
 61 |   separate(user_location, into = c("city", "state"), sep = ", ")
 62 | 
 63 | 
 64 | 
 65 | # top 10 cities
 66 | top_cities <- arrange(locations, desc(count))
 67 | 
 68 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
 69 | print("cities")
 70 | top_cities %>%
 71 |   select(city, count) %>%
 72 |   group_by(city) %>%
 73 |   head(10)
 74 | txtStop()
 75 | 
 76 | 
 77 | # top 10 States
 78 | top_states <- locations %>% 
 79 |   group_by(state) %>% 
 80 |   summarise(count = sum(count))
 81 | 
 82 | top_states <- arrange(top_states, desc(count))
 83 | 
 84 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
 85 | print("states")
 86 | top_states %>%
 87 |   head(10)
 88 | txtStop()
 89 | 
 90 | 
 91 | 
 92 | # world map
 93 | #png("world_map.png", width = 1920, height = 1080, res=300)
 94 | #matched <- joinCountryData2Map(top_states, joinCode="NAME", nameJoinColumn="state")
 95 | #tryCatch(mapCountryData(matched, nameColumnToPlot="state", mapTitle="User Location Distribution", catMethod = "pretty", colourPalette = "heat"), error = function(e) print(e))
 96 | #dev.off()
 97 | 
 98 | review_words <- df %>%
 99 |   distinct(review_body, .keep_all = TRUE) %>%
100 |   unnest_tokens(word, review_body, token="regex", pattern="[\\s\\.\\,\\-\\;\\:\\_\\?\\!\\'\\\"\\/\\(\\)\\[\\]\\{\\}\\+\\=\\<\\>\\$\\???]+",drop = FALSE) %>%
101 |   distinct(ID, word, .keep_all = TRUE) %>%
102 |   anti_join(stop_words, by = "word") %>%
103 |   filter(str_detect(word, "[^\\d]")) %>%
104 |   group_by(word) %>%
105 |   mutate(word_total = n()) %>%
106 |   ungroup()
107 | 
108 | # most common words with wordclouds
109 | png("25_wordclouds.png", width = 1920, height = 1080, res=300)
110 | word_counts <- review_words %>%
111 |   count(word, sort = TRUE) %>%
112 |   with(wordcloud(word, n, max.words = 50))
113 |   dev.off()
114 | 
115 | word_counts <- review_words %>%
116 |   count(word, sort = TRUE) 
117 | 
118 | 
119 | png("25_most_common_words.png", width = 1920, height = 1080, res=300)
120 | word_counts %>%
121 |   head(25) %>%
122 |   mutate(word = wordStem(word)) %>% 
123 |   mutate(word = reorder(word, n)) %>% 
124 |   ggplot(aes(word, n)) +
125 |   geom_col(fill = "lightblue") +
126 |   scale_y_continuous(labels = comma_format()) +
127 |   coord_flip() +
128 |   labs(title = "Most common words in reviews to date",
129 |        subtitle = "Stop words removed and stemmed",
130 |        y = "Number of uses")
131 |   dev.off()
132 | 
133 | 
134 | 
135 | 
136 | # bigrams
137 | review_bigrams <- df %>%
138 |   unnest_tokens(bigram, review_body, token = "ngrams", n = 2)
139 | 
140 | bigrams_separated <- review_bigrams %>%
141 |   separate(bigram, c("word1", "word2"), sep = " ")
142 | 
143 | bigrams_filtered <- bigrams_separated %>%
144 |   filter(!word1 %in% stop_words$word) %>%
145 |   filter(!word2 %in% stop_words$word)
146 | 
147 | bigram_counts <- bigrams_filtered %>% 
148 |   count(word1, word2, sort = TRUE)
149 | 
150 | bigrams_united <- bigrams_filtered %>%
151 |   unite(bigram, word1, word2, sep = " ")
152 | 
153 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
154 | bigrams_united %>%
155 |   count(bigram, sort = TRUE) %>%
156 |   head(10)
157 | txtStop()
158 | 
159 | 
160 | 
161 | # trigrams
162 | review_trigrams <- df %>%
163 |   unnest_tokens(trigram, review_body, token = "ngrams", n = 3)
164 | 
165 | trigrams_separated <- review_trigrams %>%
166 |   separate(trigram, c("word1", "word2", "word3"), sep = " ")
167 | 
168 | trigrams_filtered <- trigrams_separated %>%
169 |   filter(!word1 %in% stop_words$word) %>%
170 |   filter(!word2 %in% stop_words$word) %>%
171 |   filter(!word3 %in% stop_words$word)
172 | 
173 | trigram_counts <- trigrams_filtered %>% 
174 |   count(word1, word2, word3, sort = TRUE)
175 | 
176 | trigrams_united <- trigrams_filtered %>%
177 |   unite(trigram, word1, word2, word3, sep = " ")
178 | 
179 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
180 | trigrams_united %>%
181 |   count(trigram, sort = TRUE) %>%
182 |   head(10)
183 | txtStop()
184 | 
185 | 
186 | 
187 | 
188 | # growing reviews
189 | reviews_per_month <- df %>%
190 |   group_by(month) %>%
191 |   summarize(month_total = n())
192 | 
193 | word_month_counts <- review_words %>%
194 |   filter(word_total >= 10) %>%
195 |   count(word, month) %>%
196 |   tidyr::complete(word, month, fill = list(n = 0)) %>%
197 |   inner_join(reviews_per_month, by = "month") %>%
198 |   mutate(percent = n / month_total) %>%
199 |   mutate(year = year(month) + yday(month) / 365)
200 | 
201 | 
202 | 
203 | mod <- ~ glm(cbind(n, month_total - n) ~ year, ., family = "binomial")
204 | 
205 | slopes <- word_month_counts %>%
206 |   nest(-word) %>%
207 |   mutate(model = map(data, mod)) %>%
208 |   unnest(map(model, tidy)) %>%
209 |   filter(term == "year") %>%
210 |   arrange(desc(estimate))
211 | 
212 | 
213 | png("9_growing.png", width = 1920, height = 1080, res=300)
214 | slopes %>%
215 |   head(9) %>%
216 |   inner_join(word_month_counts, by = "word") %>%
217 |   mutate(word = reorder(word, -estimate)) %>%
218 |   ggplot(aes(month, n / month_total, color = word)) +
219 |   geom_line(show.legend = FALSE) +
220 |   scale_y_continuous(labels = percent_format()) +
221 |   facet_wrap(~ word, scales = "free_y") +
222 |   expand_limits(y = 0) +
223 |   labs(x = "Year",
224 |        y = "Percentage of reviews containing this word",
225 |        title = "9 fastest growing words in TripAdvisor reviews")
226 |   dev.off()
227 | 
228 | 
229 | # decreasing
230 | png("9_decreasing.png", width = 1920, height = 1080, res=300)
231 | slopes %>%
232 |   tail(9) %>%
233 |   inner_join(word_month_counts, by = "word") %>%
234 |   mutate(word = reorder(word, estimate)) %>%
235 |   ggplot(aes(month, n / month_total, color = word)) +
236 |   geom_line(show.legend = FALSE) +
237 |   scale_y_continuous(labels = percent_format()) +
238 |   facet_wrap(~ word, scales = "free_y") +
239 |   expand_limits(y = 0) +
240 |   labs(x = "Year",
241 |        y = "Percentage of reviews containing this term",
242 |        title = "9 fastest shrinking words in TripAdvisor reviews")
243 |   dev.off()
244 | 
245 | 
246 | # SENTIMENT ANALYSIS
247 | # most common positive and negative words
248 | reviews <- df %>% 
249 |   filter(!is.na(review_body)) %>% 
250 |   select(ID, review_body) %>% 
251 |   group_by(row_number()) %>% 
252 |   ungroup()
253 | tidy_reviews <- reviews %>%
254 |   unnest_tokens(word, review_body, token="regex", pattern="[\\s\\.\\,\\-\\;\\:\\_\\?\\!\\'\\\"\\/\\(\\)\\[\\]\\{\\}\\+\\=\\<\\>\\$\\???]+") 
255 |   
256 | tidy_reviews <- tidy_reviews %>%
257 |   anti_join(stop_words, by="word")
258 | 
259 | bing_word_counts <- tidy_reviews %>%
260 |   inner_join(get_sentiments("bing"), by="word") %>%
261 |   count(word, sentiment, sort = TRUE) %>%
262 |   ungroup()
263 | 
264 | png("sent_bing.png", width = 1920, height = 1080, res=300)
265 | bing_word_counts %>%
266 |   group_by(sentiment) %>%
267 |   top_n(10, n) %>%
268 |   ungroup() %>%
269 |   mutate(word = reorder(word, n)) %>%
270 |   ggplot(aes(word, n, fill = sentiment)) +
271 |   geom_col(show.legend = FALSE) +
272 |   facet_wrap(~sentiment, scales = "free") +
273 |   labs(y = "Contribution to sentiment", x = NULL) +
274 |   coord_flip() + 
275 |   ggtitle('Words with the greatest contributions to positive/negative 
276 |           sentiment in reviews [BING]') +
277 |   geom_col(show.legend = FALSE)
278 |   dev.off()
279 | 
280 | # AFINN library
281 | png("sent_afinn.png", width = 1920, height = 1080, res=300)
282 | contributions <- tidy_reviews %>%
283 |   inner_join(get_sentiments("afinn"), by = "word") %>%
284 |   group_by(word) %>%
285 |   summarize(occurences = n(), contribution = sum(score))
286 |   contributions %>%
287 |   top_n(20, abs(contribution)) %>%
288 |   mutate(word = reorder(word, contribution)) %>%
289 |   ggplot(aes(word, contribution, fill = contribution > 0)) +
290 |   ggtitle('Words with the greatest contributions to positive/negative 
291 |           sentiment in reviews [AFINN]') +
292 |   geom_col(show.legend = FALSE) +
293 |   coord_flip()
294 |   dev.off()
295 | 
296 | 
297 | # preceeded by not
298 | bigrams_separated %>%
299 |   filter(word1 == "not") %>%
300 |   count(word1, word2, sort = TRUE)
301 | 
302 | AFINN <- get_sentiments("afinn")
303 | not_words <- bigrams_separated %>%
304 |   filter(word1 == "not") %>%
305 |   inner_join(AFINN, by = c(word2 = "word")) %>%
306 |   count(word2, score, sort = TRUE) %>%
307 |   ungroup()
308 | 
309 | 
310 | not_words %>%
311 |   mutate(contribution = n * score) %>%
312 |   arrange(desc(abs(contribution))) %>%
313 |   head(20) %>%
314 |   mutate(word2 = reorder(word2, contribution)) %>%
315 |   ggplot(aes(word2, n * score, fill = n * score > 0)) +
316 |   geom_col(show.legend = FALSE) +
317 |   xlab("Words preceded by \"not\"") +
318 |   ylab("Sentiment score * number of occurrences") +
319 |   ggtitle('The 20 words preceded by "not" that had the greatest contribution to 
320 |           sentiment scores, positive or negative direction') +
321 |   coord_flip()
322 | 
323 | 
324 | # negation with no, not, never and without
325 | negation_words <- c("not", "no", "never", "without")
326 | 
327 | negated_words <- bigrams_separated %>%
328 |   filter(word1 %in% negation_words) %>%
329 |   inner_join(AFINN, by = c(word2 = "word")) %>%
330 |   count(word1, word2, score, sort = TRUE) %>%
331 |   ungroup()
332 | 
333 | png("not_follows.png", width = 1920, height = 1080, res=300)
334 | negated_words %>%
335 |   mutate(contribution = n * score,
336 |          word2 = reorder(paste(word2, word1, sep = "__"), contribution)) %>%
337 |   group_by(word1) %>%
338 |   top_n(12, abs(contribution)) %>%
339 |   ggplot(aes(word2, contribution, fill = n * score > 0)) +
340 |   geom_col(show.legend = FALSE) +
341 |   facet_wrap(~ word1, scales = "free") +
342 |   scale_x_discrete(labels = function(x) gsub("__.+$", "", x)) +
343 |   xlab("Words preceded by negation term") +
344 |   ylab("Sentiment score * number of occurrences") +
345 |   ggtitle('The most common positive and negative words which follow negations 
346 |           such as "no", "not", "never" and "without"') +
347 |   coord_flip()
348 |   dev.off()
349 | 
350 | 
351 | 
352 | # lastly, let's find out the most positive and negative reviews
353 | sentiment_messages <- tidy_reviews %>%
354 |   inner_join(get_sentiments("afinn"), by = "word") %>%
355 |   group_by(ID) %>%
356 |   summarize(sentiment = mean(score),
357 |             words = n()) %>%
358 |   ungroup() %>%
359 |   filter(words >= 5)
360 | 
361 | # most positive reviews
362 | sentiment_messages %>%
363 |   arrange(desc(sentiment)) %>%
364 |   head(1) -> i
365 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
366 | print("pos_rev")
367 | df[ which(df$ID==i$ID), ]$review_body[1]
368 | txtStop()
369 | 
370 | # most negative reviews
371 | sentiment_messages %>%
372 |   arrange(sentiment) %>%
373 |   head(1) -> i
374 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
375 | print("neg_rev")
376 | df[ which(df$ID==i$ID), ]$review_body[1]
377 | txtStop()
378 |   
379 | 


--------------------------------------------------------------------------------
/Rinput/it.R:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env Rscript
  2 | args = commandArgs(trailingOnly=TRUE)
  3 | args <- commandArgs(TRUE)
  4 | filename <- as.character(args[1])
  5 | library(dplyr) 
  6 | library(readr) 
  7 | library(lubridate)
  8 | library(ggplot2) 
  9 | library(tidytext) 
 10 | library(tidyverse) 
 11 | library(stringr) 
 12 | library(tidyr) 
 13 | library(scales) 
 14 | library(broom) 
 15 | library(purrr) 
 16 | library(widyr) 
 17 | library(igraph) 
 18 | library(ggraph) 
 19 | library(SnowballC) 
 20 | library(wordcloud) 
 21 | library(reshape2) 
 22 | library(TextWiller)
 23 | library(TeachingDemos)
 24 | library(rworldmap)
 25 | theme_set(theme_minimal()) 
 26 | 
 27 | 
 28 | # load file of sentiments
 29 | sentiments <- read_csv("Rinput/sent.csv")
 30 | load(file = "Rinput/itastopwords.rda")
 31 | 
 32 | 
 33 | # read the CSV file
 34 | df <- read_csv(filename)
 35 | Sys.setlocale("LC_TIME", "C")
 36 | df$review_date <- as.Date(df$review_date,format = "%B %d, %Y")
 37 | setwd("Routput")
 38 | txtStart("out.txt", commands=FALSE, results=TRUE, append=FALSE, visible.only=FALSE)
 39 | print("it")
 40 | dim(df); min(df$review_date); max(df$review_date)
 41 | txtStop()
 42 | 
 43 | # number reviews per week
 44 | png("rev_per_week.png", width = 1920, height = 1080, res=300)
 45 | df %>%
 46 |   count(Week = round_date(review_date, "week")) %>%
 47 |   ggplot(aes(Week, n)) +
 48 |   geom_line() + 
 49 |   ggtitle('Numero di recensioni per settimana') + 
 50 |   labs(x = "Settimana",
 51 |        y = "Numero recensioni")
 52 |   dev.off()
 53 | 
 54 | # most common word stemmed
 55 | df <- tibble::rowid_to_column(df, "ID")
 56 | df <- df %>%
 57 |   mutate(review_date = as.POSIXct(review_date, origin = "1970-01-01"),month = round_date(review_date, "month"))
 58 | 
 59 | 
 60 | # locations
 61 | locations <- df %>%
 62 |   group_by(user_location) %>%
 63 |   summarise(count = length(user_location))
 64 | 
 65 | # cities and States
 66 | locations <- locations %>%
 67 |   separate(user_location, into = c("city", "state"), sep = ", ")
 68 | 
 69 | # top 10 cities
 70 | top_cities <- arrange(locations, desc(count))
 71 | 
 72 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
 73 | print("cities")
 74 | top_cities %>%
 75 |   select(city, count) %>%
 76 |   group_by(city) %>%
 77 |   head(10)
 78 | txtStop()
 79 | 
 80 | 
 81 | # top 10 States
 82 | top_states <- locations %>%
 83 |   group_by(state) %>%
 84 |   summarise(count = sum(count))
 85 | 
 86 | top_states <- arrange(top_states, desc(count))
 87 | 
 88 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
 89 | print("states")
 90 | top_states %>%
 91 |   head(10)
 92 | txtStop()
 93 | 
 94 | # world map
 95 | #png("world_map.png", width = 1920, height = 1080, res=300)
 96 | #matched <- joinCountryData2Map(top_states, joinCode="NAME", nameJoinColumn="state")
 97 | #tryCatch(mapCountryData(matched, nameColumnToPlot="state", mapTitle="User Location Distribution", catMethod = "pretty", colourPalette = "heat"), error = function(e) print(e))
 98 | #dev.off()
 99 | 
100 | 
101 | stopword <- tibble(word=itastopwords)
102 | 
103 | review_words <- df %>%
104 |   distinct(review_body, .keep_all = TRUE) %>%
105 |   unnest_tokens(word, review_body, token="regex", pattern="[\\s\\.\\,\\-\\;\\:\\_\\?\\!\\'\\\"\\/\\(\\)\\[\\]\\{\\}\\+\\=\\<\\>\\$\\???]+") %>%
106 |   distinct(ID, word, .keep_all = TRUE) %>%
107 |   anti_join(stopword, by = "word") %>% #removes Italian stepwords
108 |   filter(str_detect(word, "[^\\d]")) %>%
109 |   group_by(word) %>%
110 |   mutate(word_total = n()) %>%
111 |   ungroup()
112 | 
113 | # most common words with wordclouds
114 | png("25_wordclouds.png", width = 1920, height = 1080, res=300)
115 | word_counts <- review_words %>%
116 |   count(word, sort = TRUE) %>%
117 |   with(wordcloud(word, n, max.words = 50))
118 |   dev.off()
119 | 
120 | word_counts <- review_words %>%
121 |   count(word, sort = TRUE)
122 | 
123 | # 25 most common words
124 | png("25_most_common_words.png", width = 1920, height = 1080, res=300)
125 | word_counts %>%
126 |   head(25) %>%
127 |   mutate(word = wordStem(word, "it")) %>% 
128 |   mutate(word = reorder(word, n)) %>%
129 |   ggplot(aes(word, n)) +
130 |   geom_col(fill = "lightblue") +
131 |   scale_y_continuous(labels = comma_format()) +
132 |   coord_flip() +
133 |   labs(title = "Parole maggiormente utilizzate",
134 |        subtitle = "Stop word rimosse, stemming delle parole",
135 |        x = "Parola",
136 |        y = "Numero di occorrenze")
137 |   dev.off()
138 | 
139 | # biagrams
140 | review_bigrams <- df %>%
141 |   unnest_tokens(bigram, review_body, token = "ngrams", n = 2)
142 | 
143 | bigrams_separated <- review_bigrams %>%
144 |   separate(bigram, c("word1", "word2"), sep = " ")
145 | 
146 | bigrams_filtered <- bigrams_separated %>%
147 |   filter(!word1 %in% stopword$word) %>%
148 |   filter(!word2 %in% stopword$word)
149 | 
150 | bigram_counts <- bigrams_filtered %>% 
151 |   count(word1, word2, sort = TRUE)
152 | 
153 | bigrams_united <- bigrams_filtered %>%
154 |   unite(bigram, word1, word2, sep = " ")
155 | 
156 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
157 | bigrams_united %>%
158 |   count(bigram, sort = TRUE)  %>%
159 |   head(10) # display only first 10 results
160 | txtStop()
161 | 
162 | 
163 | # trigrams
164 | review_trigrams <- df %>%
165 |   unnest_tokens(trigram, review_body, token = "ngrams", n = 3)
166 | 
167 | trigrams_separated <- review_trigrams %>%
168 |   separate(trigram, c("word1", "word2", "word3"), sep = " ")
169 | 
170 | trigrams_filtered <- trigrams_separated %>%
171 |   filter(!word1 %in% stopword$word) %>%
172 |   filter(!word2 %in% stopword$word) %>%
173 |   filter(!word3 %in% stopword$word)
174 | 
175 | trigram_counts <- trigrams_filtered %>% 
176 |   count(word1, word2, word3, sort = TRUE)
177 | 
178 | trigrams_united <- trigrams_filtered %>%
179 |   unite(trigram, word1, word2, word3, sep = " ")
180 | 
181 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
182 | trigrams_united %>%
183 |   count(trigram, sort = TRUE) %>%
184 |   head(10) # display only first 10 results
185 | txtStop()
186 | 
187 | 
188 | 
189 | # increasing words per months
190 | reviews_per_month <- df %>%
191 |   group_by(month) %>%
192 |   summarize(month_total = n())
193 | 
194 | 
195 | word_month_counts <- review_words %>%
196 |   filter(word_total >= 10) %>%
197 |   count(word, month) %>%
198 |   tidyr::complete(word, month, fill = list(n = 0)) %>%
199 |   inner_join(reviews_per_month, by = "month") %>%
200 |   mutate(percent = n / month_total) %>%
201 |   mutate(year = year(month) + yday(month) / 365)
202 | 
203 | 
204 | mod <- ~ glm(cbind(n, month_total - n) ~ year, ., family = "binomial")
205 | 
206 | slopes <- word_month_counts %>%
207 |   nest(-word) %>%
208 |   mutate(model = map(data, mod)) %>%
209 |   unnest(map(model, tidy)) %>%
210 |   filter(term == "year") %>%
211 |   arrange(desc(estimate))
212 | 
213 | png("9_growing.png", width = 1920, height = 1080, res=300)
214 | slopes %>%
215 |   head(9) %>%
216 |   inner_join(word_month_counts, by = "word") %>%
217 |   mutate(word = reorder(word, -estimate)) %>%
218 |   ggplot(aes(month, n / month_total, color = word)) +
219 |   geom_line(show.legend = FALSE) +
220 |   scale_y_continuous(labels = percent_format()) +
221 |   facet_wrap(~ word, scales = "free_y") +
222 |   expand_limits(y = 0) +
223 |   labs(x = "Anno",
224 |        y = "Percentuale di recensioni contenenti questa parola",
225 |        title = "Parole che sono cresciute maggiormente")
226 |   dev.off()
227 | 
228 | # decreasing word per month
229 | png("9_decreasing.png", width = 1920, height = 1080, res=300)
230 | slopes %>%
231 |   tail(9) %>%
232 |   inner_join(word_month_counts, by = "word") %>%
233 |   mutate(word = reorder(word, estimate)) %>%
234 |   ggplot(aes(month, n / month_total, color = word)) +
235 |   geom_line(show.legend = FALSE) +
236 |   scale_y_continuous(labels = percent_format()) +
237 |   facet_wrap(~ word, scales = "free_y") +
238 |   expand_limits(y = 0) +
239 |   labs(x = "Anno",
240 |        y = "Percentuale di recensioni contenenti questa parola",
241 |        title = "Parole che sono diminuite maggiormente")
242 |   dev.off()
243 | 
244 | 
245 | # SENTIMENT ANALYSIS
246 | # most common positive and negative words
247 | reviews <- df %>% 
248 |   filter(!is.na(review_body)) %>% 
249 |   select(ID, review_body) %>% 
250 |   group_by(row_number()) %>% 
251 |   ungroup()
252 | 
253 | tidy_reviews <- reviews %>%
254 |   unnest_tokens(word, review_body, token="regex", pattern="[\\s\\.\\,\\-\\;\\:\\_\\?\\!\\'\\\"\\/\\(\\)\\[\\]\\{\\}\\+\\=\\<\\>\\$\\???]+") 
255 | 
256 | tidy_reviews <- tidy_reviews %>%
257 |   anti_join(stopword, by="word")
258 | 
259 | stemmed <- tidy_reviews %>%
260 |   mutate(word = wordStem(word, "it"))
261 | 
262 | contributions <- stemmed %>%
263 |   inner_join(sentiments, by = "word") %>%
264 |   group_by(word) %>%
265 |   summarize(occurences = n(), contribution = sum(score))
266 | 
267 | png("sent_afinn.png", width = 1920, height = 1080, res=300)
268 | contributions %>%
269 |   top_n(20, abs(contribution)) %>%
270 |   mutate(word = reorder(word, contribution)) %>%
271 |   ggplot(aes(word, contribution, fill = contribution > 0)) +
272 |   ggtitle('Parole piu connesse a sentimenti positivi/negativi') +
273 |   xlab("Parola") +
274 |   ylab("Numero di occorrenze") +
275 |   geom_col(show.legend = FALSE) +
276 |   coord_flip()
277 |   dev.off()
278 | 
279 | 
280 | # preceeded by not
281 | bigrams_filtered %>%
282 |   filter(word1 == "non") %>%
283 |   count(word1, word2, sort = TRUE)
284 | 
285 | not_words <- trigrams_filtered %>%
286 |   filter(word1 == "non") %>%
287 |   inner_join(sentiments, by = c(word2 = "word")) %>%
288 |   count(word2, score, sort = TRUE) %>%
289 |   ungroup()
290 | 
291 | # negation with no, not, never and without
292 | negation_words <- c("non", "no", "ne", "neppure", "neanche", "nemmeno", "mai", "senza")
293 | 
294 | 
295 | negated_words <- bigrams_filtered %>%
296 |   filter(word1 %in% negation_words) %>%
297 |   #inner_join(sentiments, by = c(word2 = "word")) %>% eheheh
298 |   count(word1, word2, sort = TRUE) %>%
299 |   ungroup()
300 | 
301 | negated_words %>% head(10)
302 | 
303 | 
304 | png("25_not_follows.png", width = 1920, height = 1080, res=300)
305 | negated_words %>%
306 |   head(25) %>%
307 |   mutate(word = reorder(paste(word1,word2, sep=" "), n)) %>%
308 |   ggplot(aes(word, n)) +
309 |   geom_col(fill = "brown1") +
310 |   scale_y_continuous(labels = comma_format()) +
311 |   coord_flip() +
312 |   labs(title = "Parole seguite da negazioni",
313 |        x = "Parola",
314 |        y = "Numero di occorrenze")
315 | dev.off()
316 | 
317 | 
318 | # with contributions of sentiments
319 | negated_words_sent <- bigrams_filtered %>%
320 |   filter(word1 %in% negation_words) %>%
321 |   inner_join(sentiments, by = c(word2 = "word")) %>% 
322 |   count(word1, word2, score, sort = TRUE) %>%
323 |   ungroup()
324 | 
325 | 
326 | x <- tryCatch(eval(parse(text = '
327 | file.create("not_follows.png")
328 | png("not_follows.png", width = 1920, height = 1080, res=300)
329 | negated_words_sent %>%
330 |   mutate(contribution = n * score,
331 |          word2 = reorder(paste(word2, word1, sep = "__"), contribution)) %>%
332 |   group_by(word1) %>%
333 |   top_n(12, abs(contribution)) %>%
334 |   ggplot(aes(word2, contribution, fill = n * score > 0)) +
335 |   geom_col(show.legend = FALSE) +
336 |   facet_wrap(~ word1, scales = "free") +
337 |   scale_x_discrete(labels = function() gsub("__.+$", "", x)) +
338 |   xlab("Word") +
339 |   ylab("Score") +
340 |   coord_flip()')), error = function(e) e)
341 | 
342 | # here we have a permission denied error.. lol
343 | tryCatch(print(x), error = function(e) file.remove("not_follows.png"))
344 | 
345 | # lastly, let's find out the most positive and negative reviews
346 | sentiment_messages <- stemmed %>%
347 |   inner_join(sentiments, by = "word") %>%
348 |   group_by(ID) %>%
349 |   summarize(sentiment = mean(score), words = n()) %>%
350 |   ungroup() %>%
351 |   filter(words >= 5)
352 | 
353 | # most positive reviews
354 | sentiment_messages %>%
355 |   arrange(desc(sentiment)) %>%
356 |   head(1) -> i
357 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
358 | print("pos_rev")
359 | df[ which(df$ID==i$ID), ]$review_body[1]
360 | txtStop()
361 | 
362 | # most negative reviews
363 | sentiment_messages %>%
364 |   arrange(sentiment) %>%
365 |   head(1) -> i
366 | txtStart("out.txt", commands=FALSE, results=TRUE, append=TRUE, visible.only=FALSE)
367 | print("neg_rev")
368 | df[ which(df$ID==i$ID), ]$review_body[1]
369 | txtStop()
370 | 
371 | 


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/Rinput/itastopwords.rda:
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https://raw.githubusercontent.com/0x0be/scrapeadvisor/fac295ff99e2428e7ce8c8875f1bd427bb101942/Rinput/itastopwords.rda


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/Rinput/sent.csv:
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   1 | word,sentiment,lexicon,score
   2 | a-cort-di-liquid,negative,bing,-1
   3 | malincuor,negative,bing,-2
   4 | abbandon,negative,bing,-2
   5 | abbatt,negative,bing,-1
   6 | abbozz,negative,bing,-1
   7 | abbrut,negative,bing,-1
   8 | abissal,negative,bing,-1
   9 | abiss,negative,bing,-1
  10 | abomin,negative,bing,-3
  11 | abominevol,negative,bing,-3
  12 | abras,negative,bing,-1
  13 | abus,negative,bing,-1
  14 | accan,negative,bing,-1
  15 | accartocc,negative,bing,-1
  16 | accatto-nagg,negative,bing,-1
  17 | accec,negative,bing,-1
  18 | accidental,negative,bing,-1
  19 | accident,negative,bing,-1
  20 | accus,negative,bing,-1
  21 | accusator,negative,bing,-1
  22 | acid,negative,bing,-1
  23 | acre,negative,bing,-1
  24 | acrimon,negative,bing,-1
  25 | addolor,negative,bing,-2
  26 | adesc,negative,bing,-1
  27 | adulter,negative,bing,-1
  28 | affam,negative,bing,-1
  29 | affatic,negative,bing,-1
  30 | afflitt,negative,bing,-1
  31 | afflizion,negative,bing,-1
  32 | affoll,negative,bing,-2
  33 | affond,negative,bing,-1
  34 | affrant,negative,bing,-2
  35 | affront,negative,bing,-1
  36 | aggrav,negative,bing,-2
  37 | aggression,negative,bing,-3
  38 | aggress,negative,bing,-3
  39 | aggressor,negative,bing,-3
  40 | aggrott-le-sopraccigl,negative,bing,-1
  41 | aggrovigl,negative,bing,-1
  42 | aggu,negative,bing,-1
  43 | agit,negative,bing,-1
  44 | agon,negative,bing,-1
  45 | agonizz,negative,bing,-1
  46 | alien,negative,bing,-1
  47 | all'oscur,negative,bing,-1
  48 | allarm,negative,bing,-1
  49 | allerg,negative,bing,-1
  50 | allocc,negative,bing,-1
  51 | allucin,negative,bing,-2
  52 | allusion,negative,bing,-1
  53 | alterc,negative,bing,-1
  54 | altezz,negative,bing,-1
  55 | alto-e-dinoccol,negative,bing,-1
  56 | alzat-di-spall,negative,bing,-1
  57 | amar,negative,bing,-2
  58 | amarezz,negative,bing,-2
  59 | ambigu,negative,bing,-1
  60 | ambivalent,negative,bing,-1
  61 | ammacc,negative,bing,-1
  62 | ammaccatur,negative,bing,-1
  63 | ammal,negative,bing,-1
  64 | amministr-mal,negative,bing,-1
  65 | ammon,negative,bing,-1
  66 | ammonitor,negative,bing,-1
  67 | ammonizion,negative,bing,-1
  68 | ammortizz,negative,bing,-1
  69 | ampoll,negative,bing,-1
  70 | amput,negative,bing,-1
  71 | a-nalfabet,negative,bing,-1
  72 | a-narc,negative,bing,-1
  73 | anem,negative,bing,-1
  74 | angosc,negative,bing,-2
  75 | angust,negative,bing,-2
  76 | animos,negative,bing,-1
  77 | annacqu,negative,bing,-1
  78 | anneg,negative,bing,-1
  79 | annichil,negative,bing,-1
  80 | annid,negative,bing,-1
  81 | annient,negative,bing,-1
  82 | annod,negative,bing,-1
  83 | annoi,negative,bing,-1
  84 | annull,negative,bing,-1
  85 | anomal,negative,bing,-1
  86 | anormal,negative,bing,-1
  87 | ansi,negative,bing,-1
  88 | ansios,negative,bing,-1
  89 | antagon,negative,bing,-1
  90 | antagonist,negative,bing,-1
  91 | anti,negative,bing,-1
  92 | anti-americ,negative,bing,-1
  93 | anti-bianc,negative,bing,-1
  94 | anti-israel,negative,bing,-1
  95 | anti-occup,negative,bing,-1
  96 | anti-social,negative,bing,-1
  97 | antieconom,negative,bing,-1
  98 | antipat,negative,bing,-3
  99 | antiqu,negative,bing,-1
 100 | antisem,negative,bing,-1
 101 | antitet,negative,bing,-1
 102 | apat,negative,bing,-2
 103 | apocaliss,negative,bing,-1
 104 | apocalitt,negative,bing,-1
 105 | apologet,negative,bing,-1
 106 | appal,negative,bing,-1
 107 | appann,negative,bing,-1
 108 | appass,negative,bing,-1
 109 | appell,negative,bing,-1
 110 | append,negative,bing,-1
 111 | appiccic,negative,bing,-1
 112 | apprension,negative,bing,-1
 113 | apprens,negative,bing,-1
 114 | approssim,negative,bing,-1
 115 | arbitrar,negative,bing,-1
 116 | arcaic,negative,bing,-1
 117 | arcan,negative,bing,-1
 118 | ardu,negative,bing,-1
 119 | arpi,negative,bing,-1
 120 | arrabb,negative,bing,-2
 121 | arrend,negative,bing,-2
 122 | arrest,negative,bing,-1
 123 | arretratezz,negative,bing,-2
 124 | arring,negative,bing,-1
 125 | arrog,negative,bing,-2
 126 | arrogant,negative,bing,-2
 127 | arruff,negative,bing,-1
 128 | arruggin,negative,bing,-2
 129 | artific,negative,bing,-1
 130 | aspett-negat,negative,bing,-2
 131 | aspr,negative,bing,-1
 132 | assalg,negative,bing,-1
 133 | assalt,negative,bing,-1
 134 | assassin,negative,bing,-1
 135 | assecond,negative,bing,-1
 136 | assed,negative,bing,-1
 137 | assent,negative,bing,-1
 138 | assenz,negative,bing,-1
 139 | asset-di-sangu,negative,bing,-1
 140 | assill,negative,bing,-1
 141 | assorb,negative,bing,-1
 142 | assord,negative,bing,-2
 143 | assurd,negative,bing,-2
 144 | asten-da,negative,bing,-1
 145 | asti,negative,bing,-1
 146 | astut,negative,bing,-2
 147 | astuz,negative,bing,-2
 148 | atroc,negative,bing,-3
 149 | atrof,negative,bing,-1
 150 | attacc,negative,bing,-1
 151 | attacc-di-cuor,negative,bing,-1
 152 | attacc-furios,negative,bing,-1
 153 | attrit,negative,bing,-1
 154 | audac,negative,bing,-1
 155 | auster,negative,bing,-1
 156 | aut-golp,negative,bing,-1
 157 | aut-interess,negative,bing,-1
 158 | aut-umil,negative,bing,-1
 159 | autocr,negative,bing,-1
 160 | autocrat,negative,bing,-1
 161 | autodistrutt,negative,bing,-1
 162 | autolesion,negative,bing,-1
 163 | autoritar,negative,bing,-1
 164 | avar,negative,bing,-2
 165 | avariz,negative,bing,-2
 166 | avid,negative,bing,-2
 167 | avvent,negative,bing,-1
 168 | avversar,negative,bing,-1
 169 | avversion,negative,bing,-1
 170 | avvers,negative,bing,-2
 171 | avvert,negative,bing,-1
 172 | avvilent,negative,bing,-1
 173 | avvil,negative,bing,-1
 174 | avvit,negative,bing,-1
 175 | avvizz,negative,bing,-1
 176 | avvolt,negative,bing,-1
 177 | babbe,negative,bing,-1
 178 | bagarin,negative,bing,-1
 179 | baglior,negative,bing,-1
 180 | banal,negative,bing,-2
 181 | banalizz,negative,bing,-2
 182 | band,negative,bing,-1
 183 | barbar,negative,bing,-1
 184 | barcoll,negative,bing,-1
 185 | bassezz,negative,bing,-1
 186 | bass-rating,negative,bing,-1
 187 | bastard,negative,bing,-3
 188 | baston,negative,bing,-2
 189 | battaglier,negative,bing,-1
 190 | battibecc,negative,bing,-1
 191 | batt-d'arrest,negative,bing,-1
 192 | batt,negative,bing,-1
 193 | beff,negative,bing,-1
 194 | beffard,negative,bing,-2
 195 | bellic,negative,bing,-1
 196 | belliger,negative,bing,-1
 197 | berlin,negative,bing,-1
 198 | bestemm,negative,bing,-1
 199 | bestial,negative,bing,-1
 200 | biasimevol,negative,bing,-1
 201 | bigott,negative,bing,-1
 202 | birichin,negative,bing,-1
 203 | bisogn,negative,bing,-1
 204 | bizz,negative,bing,-1
 205 | bizzarr,negative,bing,-1
 206 | blah,negative,bing,-1
 207 | bland,negative,bing,-1
 208 | blasfem,negative,bing,-1
 209 | blater,negative,bing,-1
 210 | blocc,negative,bing,-1
 211 | blocc-stradal,negative,bing,-1
 212 | boicott,negative,bing,-1
 213 | bomb,negative,bing,-1
 214 | bombard,negative,bing,-1
 215 | borbott,negative,bing,-1
 216 | bottin,negative,bing,-1
 217 | bradip,negative,bing,-1
 218 | bramos,negative,bing,-1
 219 | brav,negative,bing,-1
 220 | break-me,negative,bing,-1
 221 | break-up,negative,bing,-1
 222 | briccon,negative,bing,-1
 223 | brivid,negative,bing,-1
 224 | bronc,negative,bing,-1
 225 | brontol,negative,bing,-1
 226 | brontolon,negative,bing,-1
 227 | bruc,negative,bing,-1
 228 | brusc,negative,bing,-1
 229 | brutal,negative,bing,-1
 230 | brutalizz,negative,bing,-1
 231 | brut,negative,bing,-1
 232 | bruttezz,negative,bing,-1
 233 | brutt,negative,bing,-1
 234 | bug,negative,bing,-1
 235 | bugiard,negative,bing,-2
 236 | bui,negative,bing,-1
 237 | bull,negative,bing,-1
 238 | bullism,negative,bing,-1
 239 | buon-merc,negative,bing,-1
 240 | burattin,negative,bing,-1
 241 | burber,negative,bing,-2
 242 | burl,negative,bing,-1
 243 | buss,negative,bing,-1
 244 | bust,negative,bing,-1
 245 | cacciatorpedin,negative,bing,-1
 246 | cadent,negative,bing,-1
 247 | cad,negative,bing,-1
 248 | cad-in-disgraz,negative,bing,-1
 249 | cagn,negative,bing,-1
 250 | calam,negative,bing,-1
 251 | calamit,negative,bing,-1
 252 | calpest,negative,bing,-1
 253 | calunn,negative,bing,-2
 254 | calvar,negative,bing,-1
 255 | calviz,negative,bing,-1
 256 | campagn-pubblicitar,negative,bing,-1
 257 | cancell,negative,bing,-1
 258 | cancer,negative,bing,-1
 259 | cancr,negative,bing,-1
 260 | cannibal,negative,bing,-1
 261 | cannibalizz,negative,bing,-1
 262 | canton,negative,bing,-1
 263 | caos,negative,bing,-1
 264 | caotic,negative,bing,-1
 265 | capitol,negative,bing,-1
 266 | capitombol,negative,bing,-1
 267 | capricc,negative,bing,-1
 268 | capr-espiator,negative,bing,-1
 269 | capsiz,negative,bing,-1
 270 | carc,negative,bing,-1
 271 | carent,negative,bing,-1
 272 | carenz,negative,bing,-1
 273 | carest,negative,bing,-1
 274 | caricatur,negative,bing,-1
 275 | caric,negative,bing,-1
 276 | carneficin,negative,bing,-1
 277 | car,negative,bing,-1
 278 | carp,negative,bing,-1
 279 | carregg,negative,bing,-1
 280 | cartegg,negative,bing,-1
 281 | carton-anim,negative,bing,-1
 282 | cascant,negative,bing,-1
 283 | cass-integr,negative,bing,-1
 284 | castig,negative,bing,-1
 285 | castr,negative,bing,-1
 286 | casual,negative,bing,-1
 287 | cataclism,negative,bing,-1
 288 | catastrof,negative,bing,-1
 289 | categor,negative,bing,-1
 290 | catt-condott,negative,bing,-1
 291 | cattiver,negative,bing,-2
 292 | catt,negative,bing,-2
 293 | catt-umor,negative,bing,-2
 294 | caustic,negative,bing,-1
 295 | cautel,negative,bing,-1
 296 | cauzio-1l,negative,bing,-1
 297 | cavill,negative,bing,-1
 298 | cavit,negative,bing,-1
 299 | cazz,negative,bing,-2
 300 | cedevol,negative,bing,-1
 301 | cencios,negative,bing,-1
 302 | censur,negative,bing,-1
 303 | cepp,negative,bing,-1
 304 | cestin,negative,bing,-1
 305 | che-odi,negative,bing,-1
 306 | chius,negative,bing,-1
 307 | ciarl,negative,bing,-2
 308 | ciarlat,negative,bing,-2
 309 | cicatric,negative,bing,-1
 310 | ciec,negative,bing,-1
 311 | cigol,negative,bing,-1
 312 | cinic,negative,bing,-2
 313 | cinism,negative,bing,-2
 314 | cipigl,negative,bing,-1
 315 | circospezion,negative,bing,-1
 316 | cit-in-giudiz,negative,bing,-1
 317 | clamor,negative,bing,-1
 318 | clientel,negative,bing,-1
 319 | codard,negative,bing,-2
 320 | coercit,negative,bing,-1
 321 | coercizion,negative,bing,-1
 322 | coglion,negative,bing,-3
 323 | coinvolt,negative,bing,-1
 324 | coller,negative,bing,-1
 325 | collud,negative,bing,-1
 326 | collusion,negative,bing,-1
 327 | colp,negative,bing,-1
 328 | colpevol,negative,bing,-1
 329 | colp-di-scen,negative,bing,-1
 330 | coltell,negative,bing,-1
 331 | combustion,negative,bing,-1
 332 | comic,negative,bing,-1
 333 | commiser,negative,bing,-2
 334 | compless,negative,bing,-1
 335 | complet,negative,bing,-1
 336 | complic,negative,bing,-1
 337 | comport-mal,negative,bing,-1
 338 | compromess,negative,bing,-1
 339 | compuls,negative,bing,-1
 340 | comun,negative,bing,-1
 341 | con-ari-di-sfid,negative,bing,-2
 342 | con-cattiver,negative,bing,-3
 343 | con-rabb,negative,bing,-2
 344 | con-rilutt,negative,bing,-2
 345 | con-sospett,negative,bing,-1
 346 | conced,negative,bing,-1
 347 | concentr,negative,bing,-1
 348 | concession,negative,bing,-1
 349 | concess,negative,bing,-1
 350 | concis,negative,bing,-1
 351 | condann,negative,bing,-1
 352 | condiscendent,negative,bing,-1
 353 | condiscend,negative,bing,-1
 354 | confess,negative,bing,-1
 355 | confession,negative,bing,-1
 356 | conflitt,negative,bing,-2
 357 | conflittual,negative,bing,-2
 358 | confond,negative,bing,-1
 359 | confront,negative,bing,-1
 360 | confusion,negative,bing,-1
 361 | confus,negative,bing,-1
 362 | conf,negative,bing,-1
 363 | confut,negative,bing,-1
 364 | congel,negative,bing,-1
 365 | congestion,negative,bing,-1
 366 | contag,negative,bing,-1
 367 | contamin,negative,bing,-2
 368 | contenz,negative,bing,-2
 369 | contes,negative,bing,-1
 370 | contest,negative,bing,-1
 371 | contorc,negative,bing,-1
 372 | contorsion,negative,bing,-1
 373 | contradd,negative,bing,-1
 374 | contraddittor,negative,bing,-1
 375 | contraddizion,negative,bing,-1
 376 | contrariet,negative,bing,-1
 377 | contrattemp,negative,bing,-1
 378 | contravven,negative,bing,-1
 379 | controproducent,negative,bing,-1
 380 | controvers,negative,bing,-1
 381 | controvogl,negative,bing,-2
 382 | contusion,negative,bing,-1
 383 | contus,negative,bing,-1
 384 | convuls,negative,bing,-1
 385 | corrod,negative,bing,-1
 386 | corromp,negative,bing,-1
 387 | corrosion,negative,bing,-1
 388 | corros,negative,bing,-1
 389 | corrott,negative,bing,-2
 390 | corruttor,negative,bing,-1
 391 | corruttr,negative,bing,-1
 392 | corruzion,negative,bing,-1
 393 | cortin-fumogen,negative,bing,-1
 394 | cospicu,negative,bing,-1
 395 | cospir,negative,bing,-1
 396 | costern,negative,bing,-1
 397 | costos,negative,bing,-2
 398 | costrizion,negative,bing,-1
 399 | costruzion,negative,bing,-1
 400 | cov,negative,bing,-1
 401 | cramp,negative,bing,-1
 402 | craps,negative,bing,-1
 403 | crash,negative,bing,-1
 404 | crass,negative,bing,-1
 405 | credul,negative,bing,-1
 406 | credulon,negative,bing,-1
 407 | crep,negative,bing,-1
 408 | cricc,negative,bing,-1
 409 | crimi-1l,negative,bing,-1
 410 | crimin,negative,bing,-2
 411 | cris,negative,bing,-1
 412 | critic,negative,bing,-2
 413 | croll,negative,bing,-1
 414 | cronic,negative,bing,-1
 415 | crud,negative,bing,-1
 416 | crudel,negative,bing,-1
 417 | crudelt,negative,bing,-3
 418 | cuccett,negative,bing,-1
 419 | cull,negative,bing,-1
 420 | cune,negative,bing,-1
 421 | cup,negative,bing,-1
 422 | da-incub,negative,bing,-3
 423 | dan-anbil,negative,bing,-1
 424 | dann,negative,bing,-1
 425 | dan-anzion,negative,bing,-1
 426 | dannegg,negative,bing,-1
 427 | dannos,negative,bing,-2
 428 | dar-l'ostrac,negative,bing,-1
 429 | debilit,negative,bing,-1
 430 | deb,negative,bing,-1
 431 | debol,negative,bing,-1
 432 | debol-di-ment,negative,bing,-1
 433 | debolezz,negative,bing,-1
 434 | debol-di-cuor,negative,bing,-1
 435 | decad,negative,bing,-2
 436 | decadent,negative,bing,-2
 437 | declam,negative,bing,-1
 438 | declin,negative,bing,-2
 439 | decrement,negative,bing,-2
 440 | decrepitezz,negative,bing,-2
 441 | decrep,negative,bing,-2
 442 | ded,negative,bing,-1
 443 | deficient,negative,bing,-2
 444 | deflettor,negative,bing,-1
 445 | deform,negative,bing,-1
 446 | defunt,negative,bing,-1
 447 | degener,negative,bing,-1
 448 | degen,negative,bing,-1
 449 | degn,negative,bing,-1
 450 | degrad,negative,bing,-2
 451 | delinquent,negative,bing,-1
 452 | delir,negative,bing,-2
 453 | delud,negative,bing,-2
 454 | deludent,negative,bing,-2
 455 | delus,negative,bing,-2
 456 | delusion,negative,bing,-2
 457 | demol,negative,bing,-1
 458 | demon,negative,bing,-1
 459 | demoniac,negative,bing,-1
 460 | demonizz,negative,bing,-1
 461 | demoralizz,negative,bing,-1
 462 | denigr,negative,bing,-2
 463 | denigrator,negative,bing,-2
 464 | dens,negative,bing,-1
 465 | denunc,negative,bing,-2
 466 | deplor,negative,bing,-1
 467 | deplorevol,negative,bing,-3
 468 | deprav,negative,bing,-3
 469 | deprec,negative,bing,-1
 470 | depred,negative,bing,-1
 471 | depression,negative,bing,-2
 472 | depress,negative,bing,-2
 473 | deprezz,negative,bing,-1
 474 | depriment,negative,bing,-2
 475 | deprim,negative,bing,-2
 476 | depriv,negative,bing,-1
 477 | derid,negative,bing,-1
 478 | derision,negative,bing,-1
 479 | derisor,negative,bing,-1
 480 | desert,negative,bing,-1
 481 | desitit,negative,bing,-1
 482 | desol,negative,bing,-2
 483 | desolat,negative,bing,-2
 484 | despot,negative,bing,-1
 485 | desquam,negative,bing,-1
 486 | destabilizz,negative,bing,-1
 487 | deterior,negative,bing,-2
 488 | deterrent,negative,bing,-1
 489 | detest,negative,bing,-3
 490 | detratt,negative,bing,-1
 491 | detrazion,negative,bing,-1
 492 | devast,negative,bing,-1
 493 | dev,negative,bing,-1
 494 | deviazion,negative,bing,-1
 495 | di-nessun-valor,negative,bing,-1
 496 | di-rimprover,negative,bing,-1
 497 | di-scart,negative,bing,-1
 498 | di-second-class,negative,bing,-1
 499 | di-sinistr,negative,bing,-1
 500 | di-travers,negative,bing,-1
 501 | diabol,negative,bing,-1
 502 | diametral,negative,bing,-1
 503 | diamin,negative,bing,-2
 504 | diatrib,negative,bing,-1
 505 | diavoler,negative,bing,-1
 506 | dibatt,negative,bing,-1
 507 | difens,negative,bing,-1
 508 | difett,negative,bing,-1
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1080 | infett,negative,bing,-1
1081 | infezion,negative,bing,-1
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1084 | infid,negative,bing,-1
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1101 | ingiustific,negative,bing,-2
1102 | ingiustificat,negative,bing,-2
1103 | ingiustiz,negative,bing,-1
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1105 | ingombr,negative,bing,-1
1106 | ingratitudin,negative,bing,-1
1107 | ingrat,negative,bing,-1
1108 | inguard,negative,bing,-1
1109 | inib,negative,bing,-1
1110 | inibizion,negative,bing,-1
1111 | inimic,negative,bing,-1
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1114 | inimmaginabil,negative,bing,-1
1115 | inintellig,negative,bing,-1
1116 | iniqu,negative,bing,-1
1117 | innatural,negative,bing,-1
1118 | innervos,negative,bing,-2
1119 | innest,negative,bing,-1
1120 | inond,negative,bing,-1
1121 | inoper,negative,bing,-1
1122 | inopportun,negative,bing,-2
1123 | inorrid,negative,bing,-3
1124 | inospital,negative,bing,-2
1125 | inosserv,negative,bing,-1
1126 | inquiet,negative,bing,-3
1127 | inquietant,negative,bing,-3
1128 | inquietudin,negative,bing,-1
1129 | inquin,negative,bing,-1
1130 | insapon,negative,bing,-1
1131 | insaz,negative,bing,-1
1132 | insensat,negative,bing,-1
1133 | insens,negative,bing,-1
1134 | insensibil,negative,bing,-1
1135 | insett,negative,bing,-1
1136 | insicurezz,negative,bing,-1
1137 | insicur,negative,bing,-1
1138 | insid,negative,bing,-1
1139 | insignific,negative,bing,-1
1140 | insincer,negative,bing,-1
1141 | insinu,negative,bing,-1
1142 | insociabl,negative,bing,-1
1143 | insoddisfacent,negative,bing,-2
1144 | insoddisfatt,negative,bing,-2
1145 | insoddisf,negative,bing,-2
1146 | insolent,negative,bing,-1
1147 | insolit,negative,bing,-1
1148 | insol,negative,bing,-1
1149 | insolvent,negative,bing,-1
1150 | insopport,negative,bing,-2
1151 | insopportabil,negative,bing,-3
1152 | insormont,negative,bing,-1
1153 | insosten,negative,bing,-1
1154 | insoucianc,negative,bing,-1
1155 | inspieg,negative,bing,-1
1156 | instabil,negative,bing,-1
1157 | insubordin,negative,bing,-1
1158 | insudic,negative,bing,-1
1159 | insufficient,negative,bing,-1
1160 | insul,negative,bing,-1
1161 | insult,negative,bing,-3
1162 | insurrezion,negative,bing,-1
1163 | intas,negative,bing,-1
1164 | intatt,negative,bing,-1
1165 | intef,negative,bing,-1
1166 | intens,negative,bing,-1
1167 | intercett,negative,bing,-1
1168 | interess,negative,bing,-1
1169 | interess-perso-nal,negative,bing,-1
1170 | interferent,negative,bing,-1
1171 | interfer,negative,bing,-1
1172 | intermittent,negative,bing,-1
1173 | interromp,negative,bing,-1
1174 | interrott,negative,bing,-1
1175 | interru,negative,bing,-1
1176 | intimidator,negative,bing,-1
1177 | intimid,negative,bing,-1
1178 | intont,negative,bing,-1
1179 | intopp,negative,bing,-1
1180 | intorpid,negative,bing,-1
1181 | intossic,negative,bing,-1
1182 | intransigent,negative,bing,-1
1183 | intrappol,negative,bing,-1
1184 | intrapres,negative,bing,-1
1185 | intratt,negative,bing,-1
1186 | intromett,negative,bing,-1
1187 | intrusion,negative,bing,-1
1188 | inud,negative,bing,-1
1189 | inuman,negative,bing,-3
1190 | inutil,negative,bing,-1
1191 | inutilizz,negative,bing,-1
1192 | invadent,negative,bing,-2
1193 | invad,negative,bing,-1
1194 | invalid,negative,bing,-1
1195 | invan,negative,bing,-1
1196 | invasion,negative,bing,-1
1197 | invasor,negative,bing,-1
1198 | inverosimil,negative,bing,-1
1199 | invett,negative,bing,-1
1200 | invid,negative,bing,-1
1201 | invis,negative,bing,-1
1202 | involontar,negative,bing,-1
1203 | invol,negative,bing,-1
1204 | ipocris,negative,bing,-1
1205 | ipocr,negative,bing,-1
1206 | ipocrit,negative,bing,-1
1207 | ira,negative,bing,-1
1208 | irasc,negative,bing,-1
1209 | iron,negative,bing,-1
1210 | irraggiung,negative,bing,-1
1211 | irragionevol,negative,bing,-1
1212 | irragionevolezz,negative,bing,-1
1213 | irrazional,negative,bing,-1
1214 | irrecuper,negative,bing,-1
1215 | irredim,negative,bing,-1
1216 | irregol,negative,bing,-1
1217 | irregolar,negative,bing,-1
1218 | irrepar,negative,bing,-1
1219 | irreprim,negative,bing,-1
1220 | irrequietezz,negative,bing,-1
1221 | irrequiet,negative,bing,-1
1222 | irresol,negative,bing,-1
1223 | irrespons,negative,bing,-2
1224 | irresponsabil,negative,bing,-2
1225 | irretrievabl,negative,bing,-1
1226 | irrevers,negative,bing,-1
1227 | irrilev,negative,bing,-1
1228 | irrimed,negative,bing,-1
1229 | irrimediabil,negative,bing,-1
1230 | irrisolt,negative,bing,-1
1231 | irrisolv,negative,bing,-1
1232 | irrispett,negative,bing,-2
1233 | irrit,negative,bing,-1
1234 | isol,negative,bing,-1
1235 | ister,negative,bing,-1
1236 | istig,negative,bing,-1
1237 | lacon,negative,bing,-1
1238 | lament,negative,bing,-1
1239 | lamentel,negative,bing,-2
1240 | lamentevol,negative,bing,-2
1241 | languid,negative,bing,-1
1242 | langu,negative,bing,-1
1243 | languor,negative,bing,-1
1244 | lasciv,negative,bing,-1
1245 | lasc,negative,bing,-1
1246 | latenz,negative,bing,-1
1247 | lavagg-del-cervell,negative,bing,-1
1248 | lavell,negative,bing,-1
1249 | lavorett,negative,bing,-1
1250 | legatur,negative,bing,-1
1251 | legg-erron,negative,bing,-1
1252 | lei-odi,negative,bing,-1
1253 | lent,negative,bing,-1
1254 | les,negative,bing,-1
1255 | lesion,negative,bing,-1
1256 | letal,negative,bing,-1
1257 | letarg,negative,bing,-1
1258 | lezios,negative,bing,-1
1259 | libertin,negative,bing,-1
1260 | licenz,negative,bing,-1
1261 | licenzios,negative,bing,-1
1262 | limit,negative,bing,-1
1263 | lim,negative,bing,-1
1264 | line-dur,negative,bing,-1
1265 | lit,negative,bing,-1
1266 | litig,negative,bing,-1
1267 | livid,negative,bing,-1
1268 | local-prett,negative,bing,-1
1269 | lord,negative,bing,-1
1270 | lott,negative,bing,-1
1271 | luccic,negative,bing,-1
1272 | lugubr,negative,bing,-2
1273 | lunat,negative,bing,-2
1274 | lung,negative,bing,-1
1275 | lung-temp,negative,bing,-1
1276 | luog-isol,negative,bing,-1
1277 | lurc,negative,bing,-1
1278 | lutt,negative,bing,-1
1279 | luttuos,negative,bing,-1
1280 | macabr,negative,bing,-3
1281 | macc,negative,bing,-1
1282 | macell,negative,bing,-1
1283 | maciull,negative,bing,-1
1284 | magr,negative,bing,-1
1285 | mai-l'amor,negative,bing,-1
1286 | mal-concep,negative,bing,-1
1287 | mal-defin,negative,bing,-1
1288 | mal-di,negative,bing,-1
1289 | mal-di-schien,negative,bing,-1
1290 | mal-di-test,negative,bing,-1
1291 | mal-progett,negative,bing,-1
1292 | mal-utilizz,negative,bing,-1
1293 | malaccort,negative,bing,-1
1294 | malaticc,negative,bing,-1
1295 | mal,negative,bing,-1
1296 | malatt,negative,bing,-1
1297 | malavogl,negative,bing,-1
1298 | malconc,negative,bing,-1
1299 | malcontent,negative,bing,-2
1300 | maldestr,negative,bing,-1
1301 | maldicent,negative,bing,-1
1302 | maledett,negative,bing,-2
1303 | maledizion,negative,bing,-1
1304 | maleduc,negative,bing,-3
1305 | maleodor,negative,bing,-3
1306 | maless,negative,bing,-1
1307 | malevolent,negative,bing,-1
1308 | malevol,negative,bing,-2
1309 | malfattor,negative,bing,-2
1310 | malform,negative,bing,-1
1311 | malign,negative,bing,-2
1312 | malincon,negative,bing,-1
1313 | malintes,negative,bing,-1
1314 | maliz,negative,bing,-1
1315 | mals,negative,bing,-1
1316 | malsicur,negative,bing,-1
1317 | maltratt,negative,bing,-3
1318 | malvag,negative,bing,-2
1319 | manc,negative,bing,-1
1320 | mancanz,negative,bing,-1
1321 | mancanz-di-decor,negative,bing,-1
1322 | mancanz-di-legg,negative,bing,-1
1323 | mancanz-di-rispett,negative,bing,-1
1324 | manc-di-rispett,negative,bing,-1
1325 | mand-di-comparizion,negative,bing,-1
1326 | maniacal,negative,bing,-1
1327 | maniac,negative,bing,-1
1328 | manipol,negative,bing,-1
1329 | manomett,negative,bing,-1
1330 | marc,negative,bing,-1
1331 | margi-1l,negative,bing,-1
1332 | martir,negative,bing,-1
1333 | mascalzon,negative,bing,-1
1334 | massacr,negative,bing,-1
1335 | mediocr,negative,bing,-1
1336 | meger,negative,bing,-1
1337 | melodrammat,negative,bing,-1
1338 | mendic,negative,bing,-1
1339 | men-svilupp,negative,bing,-1
1340 | ment,negative,bing,-1
1341 | mercantegg,negative,bing,-1
1342 | merd,negative,bing,-1
1343 | meschin,negative,bing,-1
1344 | mett-a-repentagl,negative,bing,-1
1345 | mett-in-pericol,negative,bing,-1
1346 | mett-sott-accus,negative,bing,-1
1347 | milit,negative,bing,-1
1348 | millant,negative,bing,-1
1349 | min,negative,bing,-1
1350 | minacc,negative,bing,-2
1351 | miragg,negative,bing,-1
1352 | miscredent,negative,bing,-1
1353 | miser,negative,bing,-1
1354 | mister,negative,bing,-1
1355 | mistific,negative,bing,-1
1356 | mod-oscur,negative,bing,-1
1357 | molest,negative,bing,-1
1358 | moncon,negative,bing,-1
1359 | mond,negative,bing,-1
1360 | monell,negative,bing,-1
1361 | monoton,negative,bing,-1
1362 | monot,negative,bing,-1
1363 | morbos,negative,bing,-1
1364 | mordac,negative,bing,-1
1365 | mordent,negative,bing,-1
1366 | morent,negative,bing,-1
1367 | moribond,negative,bing,-1
1368 | mor,negative,bing,-1
1369 | mor-di-fam,negative,bing,-1
1370 | mortal,negative,bing,-1
1371 | mort,negative,bing,-1
1372 | mortific,negative,bing,-1
1373 | mortif,negative,bing,-1
1374 | mostr,negative,bing,-1
1375 | mostruos,negative,bing,-1
1376 | mot,negative,bing,-1
1377 | muff,negative,bing,-1
1378 | mult-polarizz,negative,bing,-1
1379 | muscol-flession,negative,bing,-1
1380 | mut,negative,bing,-1
1381 | nan,negative,bing,-1
1382 | naufrag,negative,bing,-1
1383 | nause,negative,bing,-2
1384 | nauseabond,negative,bing,-2
1385 | nauseant,negative,bing,-2
1386 | naus,negative,bing,-1
1387 | nebb,negative,bing,-1
1388 | nebul,negative,bing,-1
1389 | neg,negative,bing,-1
1390 | negat,negative,bing,-1
1391 | negazion,negative,bing,-1
1392 | negligent,negative,bing,-2
1393 | nemes,negative,bing,-1
1394 | nemic,negative,bing,-1
1395 | nepot,negative,bing,-1
1396 | nervos,negative,bing,-1
1397 | nevrot,negative,bing,-1
1398 | noc,negative,bing,-1
1399 | noi,negative,bing,-1
1400 | noios,negative,bing,-1
1401 | non-appropr,negative,bing,-1
1402 | non-assicur,negative,bing,-1
1403 | non-chiar,negative,bing,-1
1404 | non-competit,negative,bing,-1
1405 | non-conferm,negative,bing,-1
1406 | non-cooper,negative,bing,-1
1407 | non-cred,negative,bing,-1
1408 | non-desider,negative,bing,-1
1409 | non-destr,negative,bing,-1
1410 | non-dispon,negative,bing,-1
1411 | non-dispost,negative,bing,-1
1412 | non-divertent,negative,bing,-1
1413 | non-equo,negative,bing,-1
1414 | non-essenzial,negative,bing,-1
1415 | non-fiduc,negative,bing,-1
1416 | non-funn,negative,bing,-1
1417 | non-molt,negative,bing,-1
1418 | non-moviment,negative,bing,-1
1419 | non-necessar,negative,bing,-1
1420 | non-ortodoss,negative,bing,-1
1421 | non-pertinent,negative,bing,-1
1422 | non-plausibil,negative,bing,-1
1423 | non-pratic,negative,bing,-1
1424 | non-qualific,negative,bing,-1
1425 | non-realist,negative,bing,-1
1426 | non-rispond,negative,bing,-1
1427 | non-sicur,negative,bing,-1
1428 | non-son-d'accord,negative,bing,-1
1429 | non-support,negative,bing,-1
1430 | non-valid,negative,bing,-1
1431 | non-verit,negative,bing,-1
1432 | non-visibil,negative,bing,-1
1433 | non-vogl,negative,bing,-1
1434 | no-sens,negative,bing,-1
1435 | nostalg,negative,bing,-1
1436 | notor,negative,bing,-1
1437 | nutr-con-il-cucchiai,negative,bing,-1
1438 | nutr,negative,bing,-1
1439 | nuvol,negative,bing,-1
1440 | obbrobr,negative,bing,-3
1441 | obes,negative,bing,-1
1442 | obiezion,negative,bing,-1
1443 | obliqu,negative,bing,-1
1444 | obliter,negative,bing,-1
1445 | obsol,negative,bing,-1
1446 | occlud,negative,bing,-1
1447 | occlusion,negative,bing,-1
1448 | occlus,negative,bing,-1
1449 | odi,negative,bing,-1
1450 | odiator,negative,bing,-1
1451 | odios,negative,bing,-2
1452 | odor,negative,bing,-2
1453 | offend,negative,bing,-2
1454 | offens,negative,bing,-2
1455 | offes,negative,bing,-2
1456 | offusc,negative,bing,-1
1457 | oltragg,negative,bing,-3
1458 | oltrepass,negative,bing,-1
1459 | ombregg,negative,bing,-1
1460 | ombros,negative,bing,-1
1461 | omett,negative,bing,-1
1462 | omicid,negative,bing,-1
1463 | omission,negative,bing,-1
1464 | oner,negative,bing,-1
1465 | oppors,negative,bing,-1
1466 | opportunist,negative,bing,-1
1467 | opposizion,negative,bing,-1
1468 | oppression,negative,bing,-1
1469 | oppress,negative,bing,-1
1470 | oppressor,negative,bing,-1
1471 | oppriment,negative,bing,-2
1472 | opprim,negative,bing,-2
1473 | orda,negative,bing,-1
1474 | orfan,negative,bing,-1
1475 | orma-rud,negative,bing,-1
1476 | orrend,negative,bing,-3
1477 | orribil,negative,bing,-3
1478 | orrid,negative,bing,-3
1479 | orripil,negative,bing,-3
1480 | orror,negative,bing,-3
1481 | ortic,negative,bing,-1
1482 | oscen,negative,bing,-3
1483 | oscill,negative,bing,-1
1484 | oscur,negative,bing,-1
1485 | ossession,negative,bing,-1
1486 | ossess,negative,bing,-1
1487 | ostacol,negative,bing,-1
1488 | ostagg,negative,bing,-1
1489 | ostent,negative,bing,-1
1490 | ostil,negative,bing,-2
1491 | osti-1t,negative,bing,-1
1492 | ostin,negative,bing,-1
1493 | ostruent,negative,bing,-1
1494 | ostru,negative,bing,-1
1495 | ostruzion,negative,bing,-1
1496 | ottus,negative,bing,-1
1497 | oversiz,negative,bing,-1
1498 | pacc,negative,bing,-1
1499 | padron,negative,bing,-1
1500 | pagan,negative,bing,-1
1501 | pallid,negative,bing,-1
1502 | pandemon,negative,bing,-1
1503 | panic,negative,bing,-1
1504 | paradossal,negative,bing,-1
1505 | paralizz,negative,bing,-1
1506 | paranoi,negative,bing,-1
1507 | paran,negative,bing,-1
1508 | parass,negative,bing,-1
1509 | par,negative,bing,-1
1510 | parod,negative,bing,-1
1511 | parzial,negative,bing,-1
1512 | pass,negative,bing,-1
1513 | passiv,negative,bing,-1
1514 | pasticcion,negative,bing,-1
1515 | pastos,negative,bing,-1
1516 | patet,negative,bing,-1
1517 | paur,negative,bing,-2
1518 | pauros,negative,bing,-2
1519 | pavonegg,negative,bing,-1
1520 | pazz,negative,bing,-1
1521 | peccamin,negative,bing,-1
1522 | pecc,negative,bing,-1
1523 | pedagg,negative,bing,-1
1524 | pedant,negative,bing,-1
1525 | pegg,negative,bing,-2
1526 | peggior,negative,bing,-2
1527 | pegn,negative,bing,-1
1528 | pen,negative,bing,-1
1529 | penal,negative,bing,-1
1530 | penalizz,negative,bing,-1
1531 | pent,negative,bing,-1
1532 | perd,negative,bing,-1
1533 | perdent,negative,bing,-1
1534 | perdut,negative,bing,-1
1535 | perfid,negative,bing,-1
1536 | pericol,negative,bing,-2
1537 | pericol-di-vit,negative,bing,-1
1538 | pericolos,negative,bing,-2
1539 | per,negative,bing,-1
1540 | permal,negative,bing,-1
1541 | pernic,negative,bing,-1
1542 | perpless,negative,bing,-1
1543 | persecu,negative,bing,-1
1544 | persegu,negative,bing,-1
1545 | perseguit,negative,bing,-1
1546 | perti-1c,negative,bing,-1
1547 | perturb,negative,bing,-1
1548 | pervas,negative,bing,-1
1549 | pervers,negative,bing,-1
1550 | perversion,negative,bing,-1
1551 | pervert,negative,bing,-2
1552 | pessim,negative,bing,-3
1553 | pessimist,negative,bing,-1
1554 | pest,negative,bing,-1
1555 | pestilenzial,negative,bing,-1
1556 | pettegolezz,negative,bing,-1
1557 | pezz,negative,bing,-1
1558 | piagniste,negative,bing,-1
1559 | piagnucol,negative,bing,-1
1560 | piang,negative,bing,-1
1561 | picchettagg,negative,bing,-1
1562 | picchett,negative,bing,-1
1563 | piccolezz,negative,bing,-1
1564 | pieg,negative,bing,-1
1565 | pien-di-rimors,negative,bing,-1
1566 | pien-di-sem,negative,bing,-1
1567 | pietos,negative,bing,-3
1568 | pietrific,negative,bing,-2
1569 | pignol,negative,bing,-1
1570 | pigr,negative,bing,-1
1571 | piuttost-piccol,negative,bing,-1
1572 | pizzic,negative,bing,-1
1573 | pizzicor,negative,bing,-1
1574 | plag,negative,bing,-1
1575 | plateal,negative,bing,-1
1576 | plebe,negative,bing,-1
1577 | poc-attraent,negative,bing,-1
1578 | poc-conosc,negative,bing,-1
1579 | poc-convincent,negative,bing,-1
1580 | poc-giudiz,negative,bing,-1
1581 | poc-inclin,negative,bing,-1
1582 | polarizz,negative,bing,-1
1583 | polem,negative,bing,-1
1584 | polemizz,negative,bing,-1
1585 | polen,negative,bing,-1
1586 | polv,negative,bing,-1
1587 | polver,negative,bing,-1
1588 | pompos,negative,bing,-1
1589 | porcil,negative,bing,-2
1590 | port-lord,negative,bing,-1
1591 | port-a-cas,negative,bing,-1
1592 | portator-di-handicap,negative,bing,-1
1593 | port,negative,bing,-1
1594 | postur,negative,bing,-1
1595 | pover,negative,bing,-1
1596 | povert,negative,bing,-1
1597 | prat,negative,bing,-1
1598 | precar,negative,bing,-1
1599 | precipit,negative,bing,-1
1600 | predator,negative,bing,-1
1601 | pregiudic,negative,bing,-1
1602 | pregiudiz,negative,bing,-1
1603 | pregiudizievol,negative,bing,-1
1604 | premedit,negative,bing,-1
1605 | prem,negative,bing,-1
1606 | preoccup,negative,bing,-2
1607 | prepotent,negative,bing,-2
1608 | presuntu,negative,bing,-1
1609 | presunzion,negative,bing,-1
1610 | pretenz,negative,bing,-1
1611 | prevaric,negative,bing,-1
1612 | prigion,negative,bing,-1
1613 | prigionier,negative,bing,-1
1614 | primit,negative,bing,-1
1615 | priv,negative,bing,-1
1616 | problem,negative,bing,-1
1617 | problemat,negative,bing,-1
1618 | procrastin,negative,bing,-1
1619 | prodigal,negative,bing,-1
1620 | profan,negative,bing,-1
1621 | prof,negative,bing,-1
1622 | proibit,negative,bing,-1
1623 | proliss,negative,bing,-1
1624 | pronunc-mal,negative,bing,-1
1625 | propagand,negative,bing,-1
1626 | protest,negative,bing,-1
1627 | protratt,negative,bing,-1
1628 | provoc,negative,bing,-1
1629 | provvisor,negative,bing,-1
1630 | prurigin,negative,bing,-1
1631 | prur,negative,bing,-1
1632 | pugn,negative,bing,-1
1633 | puls,negative,bing,-1
1634 | pulsazion,negative,bing,-1
1635 | pungent,negative,bing,-1
1636 | pungol,negative,bing,-1
1637 | punibil,negative,bing,-1
1638 | pun,negative,bing,-1
1639 | punit,negative,bing,-1
1640 | puntigl,negative,bing,-1
1641 | punton,negative,bing,-1
1642 | punzon,negative,bing,-1
1643 | purtropp,negative,bing,-1
1644 | puttan,negative,bing,-2
1645 | puzz,negative,bing,-2
1646 | puzzolent,negative,bing,-2
1647 | rabb,negative,bing,-1
1648 | rabbios,negative,bing,-1
1649 | rabbrivid,negative,bing,-3
1650 | raccapricc,negative,bing,-3
1651 | rachit,negative,bing,-1
1652 | radical,negative,bing,-1
1653 | radicalizz,negative,bing,-1
1654 | rallent,negative,bing,-1
1655 | rammar,negative,bing,-1
1656 | rampant,negative,bing,-1
1657 | rampin,negative,bing,-1
1658 | rancor,negative,bing,-1
1659 | rassegn,negative,bing,-1
1660 | rastrell,negative,bing,-1
1661 | rattrist,negative,bing,-1
1662 | razz,negative,bing,-1
1663 | razzism,negative,bing,-3
1664 | razzist,negative,bing,-3
1665 | reat,negative,bing,-2
1666 | reazio-1r,negative,bing,-1
1667 | recession,negative,bing,-1
1668 | reclam,negative,bing,-1
1669 | reclusion,negative,bing,-1
1670 | regred,negative,bing,-1
1671 | regression,negative,bing,-1
1672 | regress,negative,bing,-1
1673 | reiett,negative,bing,-1
1674 | relitt,negative,bing,-1
1675 | repression,negative,bing,-1
1676 | repress,negative,bing,-1
1677 | reprim,negative,bing,-1
1678 | repulsion,negative,bing,-1
1679 | res-dei-cont,negative,bing,-1
1680 | resistent,negative,bing,-1
1681 | resping,negative,bing,-1
1682 | respint,negative,bing,-1
1683 | restritt,negative,bing,-1
1684 | restrizion,negative,bing,-1
1685 | reticent,negative,bing,-1
1686 | retor,negative,bing,-1
1687 | retroced,negative,bing,-1
1688 | revoc,negative,bing,-1
1689 | revuls,negative,bing,-1
1690 | ribalt,negative,bing,-1
1691 | ribass,negative,bing,-1
1692 | ribell,negative,bing,-1
1693 | riboll,negative,bing,-1
1694 | ricad,negative,bing,-1
1695 | ricalcitr,negative,bing,-1
1696 | ricatt,negative,bing,-1
1697 | richied-temp,negative,bing,-1
1698 | ricors,negative,bing,-1
1699 | ridicol,negative,bing,-1
1700 | ridond,negative,bing,-1
1701 | rif,negative,bing,-1
1702 | rifiut,negative,bing,-1
1703 | rigid,negative,bing,-1
1704 | rigidezz,negative,bing,-1
1705 | rigor,negative,bing,-1
1706 | rilutt,negative,bing,-1
1707 | rimors,negative,bing,-1
1708 | rimostr,negative,bing,-1
1709 | rimpiant,negative,bing,-1
1710 | rimprover,negative,bing,-1
1711 | rincul,negative,bing,-1
1712 | ring,negative,bing,-1
1713 | rinunc,negative,bing,-1
1714 | rip,negative,bing,-1
1715 | ripetit,negative,bing,-1
1716 | ripid,negative,bing,-1
1717 | riprovevol,negative,bing,-1
1718 | ripud,negative,bing,-1
1719 | ripugn,negative,bing,-1
1720 | risc,negative,bing,-1
1721 | rischios,negative,bing,-1
1722 | risent,negative,bing,-1
1723 | risent-di,negative,bing,-1
1724 | risorgent,negative,bing,-1
1725 | ristagn,negative,bing,-1
1726 | ristrett,negative,bing,-1
1727 | ritard,negative,bing,-1
1728 | ritir,negative,bing,-1
1729 | ritorn,negative,bing,-1
1730 | ritorsion,negative,bing,-1
1731 | ritort,negative,bing,-1
1732 | ritratt,negative,bing,-1
1733 | rival,negative,bing,-1
1734 | rivest-di-zuccher,negative,bing,-1
1735 | rivolt,negative,bing,-1
1736 | robb,negative,bing,-1
1737 | roccios,negative,bing,-1
1738 | rodent,negative,bing,-1
1739 | ronz,negative,bing,-1
1740 | rottam,negative,bing,-2
1741 | rott,negative,bing,-1
1742 | rottur,negative,bing,-1
1743 | rovent,negative,bing,-1
1744 | rovesc,negative,bing,-1
1745 | rovin,negative,bing,-1
1746 | rozz,negative,bing,-1
1747 | irremediabl,negative,bing,-1
1748 | rub,negative,bing,-1
1749 | rud,negative,bing,-1
1750 | rue,negative,bing,-1
1751 | ruffian,negative,bing,-1
1752 | rug,negative,bing,-1
1753 | ruggin,negative,bing,-1
1754 | rugh,negative,bing,-1
1755 | rugos,negative,bing,-1
1756 | rumor,negative,bing,-1
1757 | sabotagg,negative,bing,-1
1758 | sacchegg,negative,bing,-1
1759 | sacc,negative,bing,-1
1760 | sacrific,negative,bing,-1
1761 | saltuar,negative,bing,-1
1762 | sanguin,negative,bing,-1
1763 | sanguisug,negative,bing,-1
1764 | sarcasm,negative,bing,-1
1765 | sarcast,negative,bing,-1
1766 | sardon,negative,bing,-1
1767 | satiregg,negative,bing,-1
1768 | satir,negative,bing,-1
1769 | sbadigl,negative,bing,-1
1770 | sbagl,negative,bing,-1
1771 | sbalord,negative,bing,-1
1772 | sbav,negative,bing,-1
1773 | sbavatur,negative,bing,-1
1774 | sbilanc,negative,bing,-1
1775 | sbilenc,negative,bing,-1
1776 | sbrindell,negative,bing,-1
1777 | sbrogl,negative,bing,-1
1778 | scacc,negative,bing,-1
1779 | scad,negative,bing,-2
1780 | scadent,negative,bing,-2
1781 | scandal,negative,bing,-3
1782 | scandalizz,negative,bing,-3
1783 | scappatell,negative,bing,-1
1784 | scappatoi,negative,bing,-1
1785 | scaric,negative,bing,-1
1786 | scars,negative,bing,-2
1787 | scarsezz,negative,bing,-2
1788 | scarsit,negative,bing,-2
1789 | scart,negative,bing,-2
1790 | scaten,negative,bing,-1
1791 | scettic,negative,bing,-1
1792 | scheletr,negative,bing,-1
1793 | schern,negative,bing,-1
1794 | scherz,negative,bing,-1
1795 | scherzos,negative,bing,-1
1796 | schiacc,negative,bing,-1
1797 | schiacciant,negative,bing,-1
1798 | schiaff,negative,bing,-1
1799 | schiamazz,negative,bing,-1
1800 | schi,negative,bing,-1
1801 | schiavizz,negative,bing,-1
1802 | schifos,negative,bing,-3
1803 | schif,negative,bing,-3
1804 | schiumos,negative,bing,-1
1805 | schiv,negative,bing,-1
1806 | sciatt,negative,bing,-1
1807 | scind,negative,bing,-1
1808 | sciocc,negative,bing,-2
1809 | scioccant,negative,bing,-2
1810 | sciolt,negative,bing,-1
1811 | scioper,negative,bing,-1
1812 | scission,negative,bing,-1
1813 | scomod,negative,bing,-2
1814 | scompars,negative,bing,-1
1815 | scompigl,negative,bing,-1
1816 | sconcert,negative,bing,-2
1817 | sconfess,negative,bing,-1
1818 | sconosc,negative,bing,-1
1819 | sconsiderat,negative,bing,-1
1820 | sconsideratezz,negative,bing,-1
1821 | sconsider,negative,bing,-1
1822 | sconsigl,negative,bing,-2
1823 | sconsol,negative,bing,-1
1824 | sconsolat,negative,bing,-1
1825 | scontent,negative,bing,-2
1826 | scontr,negative,bing,-2
1827 | scontros,negative,bing,-2
1828 | sconvenient,negative,bing,-2
1829 | sconvolgent,negative,bing,-1
1830 | sconvolg,negative,bing,-1
1831 | sconvolt,negative,bing,-1
1832 | scopp,negative,bing,-1
1833 | scoragg,negative,bing,-1
1834 | scorrett,negative,bing,-1
1835 | scortes,negative,bing,-2
1836 | scortic,negative,bing,-1
1837 | scoss,negative,bing,-1
1838 | scostant,negative,bing,-1
1839 | scredit,negative,bing,-1
1840 | screpol,negative,bing,-1
1841 | scricchiol,negative,bing,-1
1842 | scroccon,negative,bing,-1
1843 | scrupol,negative,bing,-1
1844 | sculacc,negative,bing,-1
1845 | scur,negative,bing,-1
1846 | scus,negative,bing,-1
1847 | sdegnos,negative,bing,-2
1848 | sdolci-natezz,negative,bing,-1
1849 | seccatur,negative,bing,-1
1850 | second-livell,negative,bing,-1
1851 | sedentar,negative,bing,-1
1852 | segret,negative,bing,-1
1853 | selvagg,negative,bing,-1
1854 | sem-ritard,negative,bing,-1
1855 | semplicist,negative,bing,-1
1856 | semplific,negative,bing,-1
1857 | semplif,negative,bing,-1
1858 | senil,negative,bing,-1
1859 | sens-di-colp,negative,bing,-1
1860 | senz-amor,negative,bing,-1
1861 | senz-cervell,negative,bing,-1
1862 | senz-compromess,negative,bing,-1
1863 | senz-document,negative,bing,-1
1864 | senz-legg,negative,bing,-1
1865 | senz-licenz,negative,bing,-1
1866 | senz-pens,negative,bing,-1
1867 | senz-piet,negative,bing,-1
1868 | senz-scop,negative,bing,-1
1869 | senz-scrupol,negative,bing,-1
1870 | senz-sens,negative,bing,-1
1871 | senz-speranz,negative,bing,-1
1872 | senz-success,negative,bing,-1
1873 | senz-vit,negative,bing,-1
1874 | separ,negative,bing,-1
1875 | servil,negative,bing,-1
1876 | set,negative,bing,-1
1877 | sfacc,negative,bing,-1
1878 | sfacciataggin,negative,bing,-2
1879 | sfacciat,negative,bing,-2
1880 | sfarfall,negative,bing,-1
1881 | sfavorevol,negative,bing,-1
1882 | sfid,negative,bing,-1
1883 | sfiduc,negative,bing,-1
1884 | sfoc,negative,bing,-1
1885 | sfog,negative,bing,-1
1886 | sfoll,negative,bing,-1
1887 | sfortun,negative,bing,-1
1888 | sfortu-1t,negative,bing,-1
1889 | sforz,negative,bing,-1
1890 | sfreg,negative,bing,-1
1891 | sfren,negative,bing,-1
1892 | sfrontatezz,negative,bing,-1
1893 | sfrutt,negative,bing,-1
1894 | sgangher,negative,bing,-1
1895 | sgoment,negative,bing,-3
1896 | sgradevol,negative,bing,-2
1897 | sgrad,negative,bing,-1
1898 | sgraziat,negative,bing,-2
1899 | sgraz,negative,bing,-1
1900 | sgretol,negative,bing,-1
1901 | sgrid,negative,bing,-1
1902 | sguazz,negative,bing,-1
1903 | si-arrend-facil,negative,bing,-1
1904 | si-blocc,negative,bing,-1
1905 | sibil,negative,bing,-1
1906 | siccit,negative,bing,-1
1907 | siep,negative,bing,-1
1908 | sindrom,negative,bing,-1
1909 | sinistr,negative,bing,-1
1910 | sintom,negative,bing,-1
1911 | situazion-critic,negative,bing,-1
1912 | situazion-di-stall,negative,bing,-1
1913 | situazion-difficil,negative,bing,-1
1914 | slav,negative,bing,-1
1915 | sleal,negative,bing,-2
1916 | slealt,negative,bing,-2
1917 | slog,negative,bing,-1
1918 | smarr,negative,bing,-1
1919 | sment,negative,bing,-1
1920 | sminu,negative,bing,-1
1921 | smod,negative,bing,-1
1922 | smodat,negative,bing,-1
1923 | smorf,negative,bing,-1
1924 | snervant,negative,bing,-2
1925 | snob,negative,bing,-1
1926 | snobb,negative,bing,-1
1927 | sob,negative,bing,-1
1928 | sobr,negative,bing,-1
1929 | soccomb,negative,bing,-1
1930 | sofferent,negative,bing,-1
1931 | soffert,negative,bing,-1
1932 | soffoc,negative,bing,-1
1933 | soffr,negative,bing,-1
1934 | sofistic,negative,bing,-1
1935 | soggezion,negative,bing,-1
1936 | soggiog,negative,bing,-1
1937 | solitar,negative,bing,-1
1938 | solitudin,negative,bing,-1
1939 | somar,negative,bing,-1
1940 | sommers,negative,bing,-1
1941 | soppression,negative,bing,-1
1942 | sopraff,negative,bing,-1
1943 | sopraffatt,negative,bing,-1
1944 | sopravval,negative,bing,-1
1945 | sopravvalut,negative,bing,-2
1946 | sord,negative,bing,-1
1947 | sornion,negative,bing,-1
1948 | sospett,negative,bing,-1
1949 | sottodimension,negative,bing,-1
1950 | sottoline,negative,bing,-1
1951 | sottomess,negative,bing,-1
1952 | sottomission,negative,bing,-1
1953 | sottopag,negative,bing,-1
1954 | sottopost,negative,bing,-1
1955 | sottoquot,negative,bing,-1
1956 | sottosquadr,negative,bing,-1
1957 | sottot,negative,bing,-1
1958 | sottrarr,negative,bing,-1
1959 | sovraccar,negative,bing,-1
1960 | sovrappes,negative,bing,-1
1961 | sovrastim,negative,bing,-1
1962 | sovversion,negative,bing,-1
1963 | sovvers,negative,bing,-1
1964 | sovvert,negative,bing,-1
1965 | spaccatur,negative,bing,-1
1966 | spadronegg,negative,bing,-1
1967 | sparg-di-sangu,negative,bing,-1
1968 | spar,negative,bing,-1
1969 | spavald,negative,bing,-1
1970 | spavent,negative,bing,-1
1971 | spaventevol,negative,bing,-2
1972 | spericol,negative,bing,-1
1973 | sperper,negative,bing,-1
1974 | spettral,negative,bing,-1
1975 | spettr,negative,bing,-1
1976 | spiacevol,negative,bing,-2
1977 | spiegazz,negative,bing,-1
1978 | spietat,negative,bing,-1
1979 | spietatezz,negative,bing,-1
1980 | spiet,negative,bing,-1
1981 | spiffer,negative,bing,-1
1982 | spinos,negative,bing,-1
1983 | spir-battaglier,negative,bing,-1
1984 | spogl,negative,bing,-1
1985 | sporad,negative,bing,-1
1986 | sporciz,negative,bing,-3
1987 | sporc,negative,bing,-3
1988 | spost,negative,bing,-1
1989 | sprec,negative,bing,-1
1990 | spregevol,negative,bing,-3
1991 | spregiat,negative,bing,-1
1992 | spregiudicat,negative,bing,-1
1993 | sprezzant,negative,bing,-1
1994 | sproloqu,negative,bing,-1
1995 | sproporzion,negative,bing,-1
1996 | spudorat,negative,bing,-1
1997 | spudoratezz,negative,bing,-1
1998 | spudor,negative,bing,-1
1999 | spur,negative,bing,-1
2000 | squallid,negative,bing,-3
2001 | squallor,negative,bing,-3
2002 | squal,negative,bing,-1
2003 | squamos,negative,bing,-1
2004 | squilibr,negative,bing,-1
2005 | squitt,negative,bing,-1
2006 | sradic,negative,bing,-1
2007 | stag-1nt,negative,bing,-1
2008 | stag-1zion,negative,bing,-1
2009 | stall,negative,bing,-1
2010 | stamped,negative,bing,-1
2011 | stamp,negative,bing,-1
2012 | stant,negative,bing,-1
2013 | static,negative,bing,-1
2014 | stereotip,negative,bing,-1
2015 | steril,negative,bing,-1
2016 | stermin,negative,bing,-1
2017 | stigm,negative,bing,-1
2018 | stigmatizz,negative,bing,-1
2019 | stizz,negative,bing,-1
2020 | stizzos,negative,bing,-1
2021 | stord,negative,bing,-1
2022 | storp,negative,bing,-1
2023 | straf,negative,bing,-1
2024 | strag,negative,bing,-1
2025 | stran,negative,bing,-1
2026 | stranezz,negative,bing,-1
2027 | strangol,negative,bing,-1
2028 | strapag,negative,bing,-1
2029 | strapazz,negative,bing,-1
2030 | strapp,negative,bing,-1
2031 | stravag,negative,bing,-1
2032 | straziant,negative,bing,-2
2033 | streg,negative,bing,-1
2034 | stress,negative,bing,-1
2035 | stressant,negative,bing,-2
2036 | strett,negative,bing,-2
2037 | strident,negative,bing,-1
2038 | strid,negative,bing,-1
2039 | stridul,negative,bing,-1
2040 | strill,negative,bing,-1
2041 | striminz,negative,bing,-1
2042 | strisc,negative,bing,-1
2043 | strisciant,negative,bing,-1
2044 | strofin,negative,bing,-1
2045 | stronz,negative,bing,-3
2046 | stropicc,negative,bing,-1
2047 | strozz,negative,bing,-1
2048 | strugg,negative,bing,-1
2049 | struggent,negative,bing,-1
2050 | stucchevol,negative,bing,-2
2051 | stupefatt,negative,bing,-1
2052 | stupid,negative,bing,-2
2053 | stupr,negative,bing,-1
2054 | subaltern,negative,bing,-1
2055 | subdol,negative,bing,-2
2056 | subordin,negative,bing,-1
2057 | succ,negative,bing,-1
2058 | sudar,negative,bing,-1
2059 | sud,negative,bing,-1
2060 | suicid,negative,bing,-1
2061 | superficial,negative,bing,-1
2062 | superflu,negative,bing,-1
2063 | superstizion,negative,bing,-1
2064 | superstiz,negative,bing,-1
2065 | supplic,negative,bing,-1
2066 | supponent,negative,bing,-2
2067 | surriscald,negative,bing,-2
2068 | suscett,negative,bing,-1
2069 | svantagg,negative,bing,-1
2070 | sventr,negative,bing,-1
2071 | svil,negative,bing,-1
2072 | svist,negative,bing,-1
2073 | svit,negative,bing,-1
2074 | svogl,negative,bing,-1
2075 | tagl,negative,bing,-1
2076 | tard,negative,bing,-1
2077 | tassazion,negative,bing,-1
2078 | tast,negative,bing,-1
2079 | tedios,negative,bing,-1
2080 | temerar,negative,bing,-1
2081 | temerariet,negative,bing,-1
2082 | temper,negative,bing,-1
2083 | tempest,negative,bing,-1
2084 | tension,negative,bing,-1
2085 | tentazion,negative,bing,-1
2086 | tentenn,negative,bing,-1
2087 | terremot,negative,bing,-1
2088 | terribil,negative,bing,-3
2089 | terror,negative,bing,-3
2090 | terrorizz,negative,bing,-3
2091 | tes,negative,bing,-1
2092 | test-cald,negative,bing,-1
2093 | testardaggin,negative,bing,-2
2094 | testard,negative,bing,-2
2095 | ti-odi,negative,bing,-1
2096 | tirann,negative,bing,-1
2097 | tirapied,negative,bing,-1
2098 | tirc,negative,bing,-1
2099 | tont,negative,bing,-1
2100 | toporagn,negative,bing,-1
2101 | torment,negative,bing,-1
2102 | torrent,negative,bing,-1
2103 | tortuos,negative,bing,-1
2104 | tortur,negative,bing,-1
2105 | tossic,negative,bing,-2
2106 | tossicodipendent,negative,bing,-1
2107 | totalitar,negative,bing,-1
2108 | traball,negative,bing,-1
2109 | trad,negative,bing,-1
2110 | traditor,negative,bing,-1
2111 | traged,negative,bing,-1
2112 | tragic,negative,bing,-1
2113 | tram,negative,bing,-1
2114 | trappol,negative,bing,-1
2115 | trasal,negative,bing,-1
2116 | trascin,negative,bing,-1
2117 | trascur,negative,bing,-1
2118 | trasecol,negative,bing,-1
2119 | trasgred,negative,bing,-1
2120 | trasgression,negative,bing,-1
2121 | trasgression-dell-legg,negative,bing,-1
2122 | trasgressor-dell-legg,negative,bing,-1
2123 | trasgressor,negative,bing,-1
2124 | trash,negative,bing,-1
2125 | traum,negative,bing,-2
2126 | traumat,negative,bing,-2
2127 | traumatizz,negative,bing,-2
2128 | travagl,negative,bing,-1
2129 | travis,negative,bing,-1
2130 | travolg,negative,bing,-1
2131 | trist,negative,bing,-2
2132 | tristement,negative,bing,-2
2133 | tristezz,negative,bing,-2
2134 | troi,negative,bing,-1
2135 | tropp-car,negative,bing,-1
2136 | tropp-mal,negative,bing,-1
2137 | trucc,negative,bing,-1
2138 | truff,negative,bing,-2
2139 | truffator,negative,bing,-2
2140 | tumult,negative,bing,-1
2141 | tumultu,negative,bing,-1
2142 | turbolent,negative,bing,-1
2143 | ubriac,negative,bing,-2
2144 | ubriacon,negative,bing,-2
2145 | uccid,negative,bing,-1
2146 | uccis,negative,bing,-1
2147 | ultimatum,negative,bing,-1
2148 | ultim-disper,negative,bing,-1
2149 | urgent,negative,bing,-1
2150 | urtand,negative,bing,-1
2151 | urtar,negative,bing,-1
2152 | urtat,negative,bing,-1
2153 | urti,negative,bing,-1
2154 | ustion,negative,bing,-1
2155 | usurp,negative,bing,-1
2156 | vacill,negative,bing,-1
2157 | vacu,negative,bing,-1
2158 | vag,negative,bing,-1
2159 | valang,negative,bing,-1
2160 | van,negative,bing,-1
2161 | vang,negative,bing,-1
2162 | vanit,negative,bing,-1
2163 | veement,negative,bing,-1
2164 | veemenz,negative,bing,-1
2165 | velen,negative,bing,-2
2166 | vendett,negative,bing,-1
2167 | vendic,negative,bing,-1
2168 | vendicat,negative,bing,-1
2169 | vergogn,negative,bing,-2
2170 | vers,negative,bing,-1
2171 | vertigin,negative,bing,-1
2172 | vescic,negative,bing,-1
2173 | vess,negative,bing,-1
2174 | viet,negative,bing,-1
2175 | vigliacc,negative,bing,-2
2176 | vil,negative,bing,-2
2177 | vilt,negative,bing,-2
2178 | violator,negative,bing,-1
2179 | violazion,negative,bing,-1
2180 | violenz,negative,bing,-2
2181 | violent,negative,bing,-2
2182 | viper,negative,bing,-1
2183 | virulent,negative,bing,-1
2184 | virus,negative,bing,-1
2185 | viscos,negative,bing,-1
2186 | vill,negative,bing,-1
2187 | vittim,negative,bing,-1
2188 | vittimizz,negative,bing,-1
2189 | viz,negative,bing,-1
2190 | vizios,negative,bing,-1
2191 | volg,negative,bing,-1
2192 | volubil,negative,bing,-1
2193 | vom,negative,bing,-1
2194 | vomit,negative,bing,-2
2195 | vulner,negative,bing,-1
2196 | vuot,negative,bing,-1
2197 | zelant,negative,bing,-1
2198 | zel,negative,bing,-1
2199 | zer,negative,bing,-1
2200 | zimbell,negative,bing,-1
2201 | zitell,negative,bing,-1
2202 | zomb,negative,bing,-1
2203 | zoppic,negative,bing,-1
2204 | zopp,negative,bing,-1
2205 | vaffancul,negative,bing,-1
2206 | affancul,negative,bing,-1
2207 | fancul,negative,bing,-1
2208 | sfig,negative,bing,-1
2209 | emotebad,negative,bing,-1
2210 | emotesick,negative,bing,-1
2211 | emotevil,negative,bing,-1
2212 | emotecry,negative,bing,-1
2213 | emoteshock,negative,bing,-1
2214 | emotedisapp,negative,bing,-1
2215 | emotesil,negative,bing,-1
2216 | emotesham,negative,bing,-1
2217 | abbagl,positive,bing,1
2218 | abbell,positive,bing,1
2219 | abbond,positive,bing,1
2220 | abbracc,positive,bing,1
2221 | abbriv,positive,bing,1
2222 | abil,positive,bing,1
2223 | accarezz,positive,bing,1
2224 | accattiv,positive,bing,1
2225 | access,positive,bing,1
2226 | acclam,positive,bing,1
2227 | accoglient,positive,bing,2
2228 | accolt,positive,bing,1
2229 | accomod,positive,bing,1
2230 | accur,positive,bing,1
2231 | acquis,positive,bing,1
2232 | acut,positive,bing,1
2233 | acutezz,positive,bing,1
2234 | adatt,positive,bing,1
2235 | addolc,positive,bing,1
2236 | adegu,positive,bing,1
2237 | ademp,positive,bing,1
2238 | ader,positive,bing,1
2239 | ador,positive,bing,3
2240 | adul,positive,bing,1
2241 | aerodin,positive,bing,1
2242 | affabil,positive,bing,1
2243 | affar,positive,bing,1
2244 | affascin,positive,bing,2
2245 | afferm,positive,bing,1
2246 | affett,positive,bing,1
2247 | affettu,positive,bing,1
2248 | affiat,positive,bing,1
2249 | affid,positive,bing,1
2250 | affin,positive,bing,1
2251 | affluent,positive,bing,1
2252 | affluenz,positive,bing,1
2253 | aggiorn,positive,bing,1
2254 | agil,positive,bing,1
2255 | ahah,positive,bing,1
2256 | aiut,positive,bing,1
2257 | alba,positive,bing,1
2258 | alla-mod,positive,bing,2
2259 | allegr,positive,bing,2
2260 | allett,positive,bing,1
2261 | allev,positive,bing,1
2262 | alliet,positive,bing,1
2263 | alta-qualit,positive,bing,3
2264 | altruist,positive,bing,2
2265 | amabil,positive,bing,2
2266 | amant,positive,bing,2
2267 | amat,positive,bing,2
2268 | amator,positive,bing,1
2269 | ambiz,positive,bing,1
2270 | amen,positive,bing,1
2271 | amichevol,positive,bing,2
2272 | amiciz,positive,bing,1
2273 | amic,positive,bing,1
2274 | ammir,positive,bing,2
2275 | ammirevol,positive,bing,2
2276 | ammiss,positive,bing,1
2277 | amo,positive,bing,3
2278 | amor,positive,bing,3
2279 | amorevol,positive,bing,3
2280 | ampi,positive,bing,1
2281 | angel,positive,bing,1
2282 | anim,positive,bing,1
2283 | apert,positive,bing,1
2284 | apertur,positive,bing,1
2285 | apot,positive,bing,1
2286 | appassionat,positive,bing,1
2287 | appassion,positive,bing,1
2288 | applaud,positive,bing,2
2289 | apprezz,positive,bing,2
2290 | apprezzabil,positive,bing,2
2291 | approv,positive,bing,1
2292 | ardent,positive,bing,1
2293 | ardor,positive,bing,1
2294 | armon,positive,bing,2
2295 | armonizz,positive,bing,2
2296 | arricc,positive,bing,1
2297 | arzill,positive,bing,1
2298 | aspir,positive,bing,1
2299 | assapor,positive,bing,1
2300 | assicur,positive,bing,1
2301 | astut,positive,bing,1
2302 | attendibil,positive,bing,1
2303 | attraent,positive,bing,2
2304 | attrazion,positive,bing,1
2305 | audac,positive,bing,2
2306 | augur,positive,bing,1
2307 | autent,positive,bing,1
2308 | autocompiac,positive,bing,1
2309 | autodetermin,positive,bing,1
2310 | autonom,positive,bing,1
2311 | autorevol,positive,bing,1
2312 | autorizz,positive,bing,1
2313 | autosufficient,positive,bing,1
2314 | avanzat,positive,bing,1
2315 | avvenent,positive,bing,1
2316 | avventur,positive,bing,1
2317 | avvincent,positive,bing,2
2318 | b-day,positive,bing,1
2319 | baglior,positive,bing,1
2320 | bass-prezz,positive,bing,1
2321 | bass-risc,positive,bing,1
2322 | beat,positive,bing,1
2323 | beatitudin,positive,bing,1
2324 | bell'aspett,positive,bing,2
2325 | bellezz,positive,bing,2
2326 | bell,positive,bing,2
2327 | mitic,positive,bing,3
2328 | ben-accolt,positive,bing,2
2329 | ben-colleg,positive,bing,2
2330 | ben-consider,positive,bing,1
2331 | ben-educ,positive,bing,2
2332 | ben-educato,positive,bing,2
2333 | ben-fatt,positive,bing,2
2334 | ben-gest,positive,bing,2
2335 | ben-inform,positive,bing,2
2336 | ben-istru,positive,bing,2
2337 | ben-posizion,positive,bing,2
2338 | ben-retroillumin,positive,bing,2
2339 | ben,positive,bing,2
2340 | bened,positive,bing,1
2341 | benedizion,positive,bing,1
2342 | beneduc,positive,bing,2
2343 | benefattor,positive,bing,2
2344 | benef,positive,bing,1
2345 | benefici,positive,bing,1
2346 | beneficiar,positive,bing,1
2347 | beness,positive,bing,2
2348 | benevolent,positive,bing,2
2349 | benevol,positive,bing,2
2350 | benifits,positive,bing,1
2351 | benven,positive,bing,1
2352 | bizzeff,positive,bing,1
2353 | bland,positive,bing,1
2354 | bollent,positive,bing,1
2355 | bontà,positive,bing,1
2356 | bonus,positive,bing,2
2357 | brav,positive,bing,2
2358 | brezz,positive,bing,1
2359 | brillant,positive,bing,3
2360 | brillantezz,positive,bing,3
2361 | brill,positive,bing,3
2362 | brios,positive,bing,1
2363 | buon-compleann,positive,bing,1
2364 | buongiorn,positive,bing,2
2365 | buon-volont,positive,bing,2
2366 | buon-intenzion,positive,bing,2
2367 | buon,positive,bing,2
2368 | buonissim,positive,bing,3
2369 | bon,positive,bing,2
2370 | buonsens,positive,bing,1
2371 | cald,positive,bing,1
2372 | calm,positive,bing,1
2373 | calmant,positive,bing,1
2374 | calor,positive,bing,1
2375 | campion,positive,bing,1
2376 | capolavor,positive,bing,1
2377 | caratterist,positive,bing,1
2378 | cariner,positive,bing,1
2379 | carin,positive,bing,1
2380 | carism,positive,bing,1
2381 | carismat,positive,bing,1
2382 | caritatevol,positive,bing,1
2383 | car,positive,bing,1
2384 | cast,positive,bing,1
2385 | cavalleresc,positive,bing,1
2386 | cavaller,positive,bing,1
2387 | celebr,positive,bing,1
2388 | celer,positive,bing,1
2389 | celest,positive,bing,1
2390 | cerimon,positive,bing,1
2391 | cherubin,positive,bing,1
2392 | chiaramente,positive,bing,1
2393 | chiarezz,positive,bing,1
2394 | ciel,positive,bing,1
2395 | civilizz,positive,bing,1
2396 | civilt,positive,bing,1
2397 | coccol,positive,bing,1
2398 | coerent,positive,bing,1
2399 | coerenz,positive,bing,1
2400 | coes,positive,bing,1
2401 | colorat,positive,bing,1
2402 | comfort,positive,bing,1
2403 | commovent,positive,bing,1
2404 | comod,positive,bing,1
2405 | competent,positive,bing,2
2406 | competit,positive,bing,1
2407 | compiacent,positive,bing,2
2408 | complement,positive,bing,1
2409 | compliment,positive,bing,2
2410 | compl,positive,bing,1
2411 | comprens,positive,bing,1
2412 | con-affett,positive,bing,2
2413 | con-ammir,positive,bing,2
2414 | con-gioi,positive,bing,2
2415 | con-gratitudin,positive,bing,2
2416 | con-success,positive,bing,2
2417 | con-tutt-il-cuor,positive,bing,2
2418 | concil,positive,bing,1
2419 | concis,positive,bing,1
2420 | confid,positive,bing,1
2421 | confortevol,positive,bing,2
2422 | confort,positive,bing,2
2423 | congenial,positive,bing,1
2424 | congratul,positive,bing,1
2425 | consolid,positive,bing,1
2426 | contentezz,positive,bing,2
2427 | content,positive,bing,2
2428 | continu,positive,bing,1
2429 | convenient,positive,bing,1
2430 | convincent,positive,bing,1
2431 | convint,positive,bing,1
2432 | coolfin qu,positive,bing,1
2433 | cooper,positive,bing,1
2434 | coragg,positive,bing,1
2435 | cordial,positive,bing,2
2436 | corrett,positive,bing,1
2437 | cortes,positive,bing,1
2438 | coscienz,positive,bing,1
2439 | cos-buon,positive,bing,1
2440 | cos-content,positive,bing,1
2441 | costant,positive,bing,1
2442 | costanz,positive,bing,1
2443 | costrutt,positive,bing,1
2444 | creat,positive,bing,1
2445 | credibil,positive,bing,1
2446 | cred,positive,bing,1
2447 | croccant,positive,bing,1
2448 | cur,positive,bing,1
2449 | da-record,positive,bing,1
2450 | deferent,positive,bing,1
2451 | deginified,positive,bing,1
2452 | degn,positive,bing,1
2453 | degn-di-not,positive,bing,2
2454 | delicatezz,positive,bing,1
2455 | delic,positive,bing,2
2456 | deliz,positive,bing,2
2457 | desider,positive,bing,1
2458 | destin,positive,bing,1
2459 | destr,positive,bing,1
2460 | devot?,positive,bing,1
2461 | di-alta-qualit,positive,bing,3
2462 | di-bell'aspett,positive,bing,2
2463 | di-cuor,positive,bing,1
2464 | di-fam-mondiale?,positive,bing,1
2465 | di-luss,positive,bing,1
2466 | di-moda?,positive,bing,1
2467 | di-prim-qualit,positive,bing,1
2468 | di-princip,positive,bing,1
2469 | di-rispost,positive,bing,1
2470 | di-success,positive,bing,1
2471 | difensor,positive,bing,1
2472 | dignit,positive,bing,1
2473 | diligent,positive,bing,1
2474 | di1m,positive,bing,1
2475 | diplomat,positive,bing,1
2476 | dispon,positive,bing,1
2477 | dispost,positive,bing,1
2478 | distint,positive,bing,1
2479 | distinzion,positive,bing,1
2480 | divertent,positive,bing,1
2481 | divert,positive,bing,1
2482 | divin,positive,bing,1
2483 | dolc,positive,bing,1
2484 | dolcement,positive,bing,1
2485 | dolcezz,positive,bing,1
2486 | domin,positive,bing,1
2487 | don-t-kill,positive,bing,1
2488 | dot,positive,bing,1
2489 | duratur,positive,bing,1
2490 | durevol,positive,bing,1
2491 | ebbrezz,positive,bing,1
2492 | eccell,positive,bing,3
2493 | eccellent,positive,bing,3
2494 | eccels,positive,bing,3
2495 | eccezional,positive,bing,3
2496 | eccit,positive,bing,1
2497 | econom,positive,bing,2
2498 | edific,positive,bing,1
2499 | educ,positive,bing,2
2500 | efficac,positive,bing,2
2501 | efficient,positive,bing,2
2502 | efficient-energet,positive,bing,1
2503 | elast,positive,bing,1
2504 | eleg,positive,bing,1
2505 | elegant,positive,bing,3
2506 | elettrific,positive,bing,1
2507 | elev,positive,bing,1
2508 | elog,positive,bing,1
2509 | eloquent,positive,bing,1
2510 | eminent,positive,bing,1
2511 | emozio1nte?,positive,bing,1
2512 | emozio1nt,positive,bing,1
2513 | emozion,positive,bing,1
2514 | empat,positive,bing,1
2515 | empatizz,positive,bing,1
2516 | encom,positive,bing,1
2517 | energet,positive,bing,1
2518 | energ,positive,bing,1
2519 | entusiasm,positive,bing,2
2520 | entusiast,positive,bing,2
2521 | equilibr,positive,bing,1
2522 | equilibrio,positive,bing,1
2523 | equit,positive,bing,1
2524 | equo,positive,bing,1
2525 | ergonom,positive,bing,1
2526 | ero,positive,bing,1
2527 | eroic,positive,bing,1
2528 | eroin,positive,bing,1
2529 | erud,positive,bing,1
2530 | esalt,positive,bing,1
2531 | esempl,positive,bing,1
2532 | esilar,positive,bing,1
2533 | espans,positive,bing,1
2534 | esser-allegr,positive,bing,1
2535 | estas,positive,bing,1
2536 | estat,positive,bing,1
2537 | esuber,positive,bing,1
2538 | esult,positive,bing,1
2539 | etern,positive,bing,1
2540 | etic,positive,bing,1
2541 | eufor,positive,bing,2
2542 | evoc,positive,bing,1
2543 | fabbric,positive,bing,1
2544 | facil,positive,bing,1
2545 | facil-da-usar,positive,bing,1
2546 | facilit,positive,bing,1
2547 | fam,positive,bing,1
2548 | famos,positive,bing,1
2549 | fan,positive,bing,1
2550 | fanfar,positive,bing,1
2551 | fantas,positive,bing,1
2552 | fantast,positive,bing,1
2553 | fascin,positive,bing,1
2554 | fattibil,positive,bing,1
2555 | faust,positive,bing,1
2556 | favol,positive,bing,3
2557 | favorevol,positive,bing,1
2558 | favor,positive,bing,1
2559 | fecond,positive,bing,1
2560 | fed,positive,bing,1
2561 | fedel,positive,bing,1
2562 | fedelt,positive,bing,2
2563 | felic,positive,bing,2
2564 | fenomenal,positive,bing,1
2565 | fertil,positive,bing,1
2566 | fervent,positive,bing,1
2567 | fervid,positive,bing,1
2568 | fervor,positive,bing,1
2569 | fest,positive,bing,1
2570 | fid,positive,bing,1
2571 | fiduc,positive,bing,2
2572 | fier,positive,bing,2
2573 | fighissim,positive,bing,3
2574 | fig,positive,bing,3
2575 | fin,positive,bing,1
2576 | finement,positive,bing,1
2577 | fior,positive,bing,1
2578 | fiorent,positive,bing,1
2579 | flessibil,positive,bing,1
2580 | fluent,positive,bing,1
2581 | focos,positive,bing,1
2582 | follegg,positive,bing,1
2583 | formicol,positive,bing,1
2584 | formid,positive,bing,1
2585 | fort,positive,bing,1
2586 | fortuit,positive,bing,1
2587 | fortu,positive,bing,1
2588 | fortun,positive,bing,1
2589 | forz-d'anim,positive,bing,1
2590 | fratern,positive,bing,1
2591 | freschissim,positive,bing,2
2592 | fresc,positive,bing,2
2593 | frugal,positive,bing,1
2594 | fulgor,positive,bing,1
2595 | futurist,positive,bing,1
2596 | futur,positive,bing,1
2597 | galant,positive,bing,1
2598 | gallegg,positive,bing,1
2599 | garant,positive,bing,1
2600 | garanz,positive,bing,1
2601 | gemm,positive,bing,1
2602 | gener,positive,bing,1
2603 | generos,positive,bing,2
2604 | genial,positive,bing,3
2605 | gen,positive,bing,1
2606 | gentilezz,positive,bing,3
2607 | gentil,positive,bing,3
2608 | giocos,positive,bing,1
2609 | gioi,positive,bing,2
2610 | gioios,positive,bing,2
2611 | giovanil,positive,bing,2
2612 | giovial,positive,bing,2
2613 | giubil,positive,bing,1
2614 | giudiz,positive,bing,1
2615 | giust,positive,bing,1
2616 | giustezz,positive,bing,1
2617 | giustiz,positive,bing,1
2618 | global,positive,bing,1
2619 | glor,positive,bing,1
2620 | glorific,positive,bing,1
2621 | glorios,positive,bing,2
2622 | god,positive,bing,1
2623 | gradevol,positive,bing,2
2624 | grad,positive,bing,1
2625 | grand,positive,bing,1
2626 | grand-capac,positive,bing,2
2627 | grandezz,positive,bing,1
2628 | grandin,positive,bing,1
2629 | grandios,positive,bing,3
2630 | gratif,positive,bing,2
2631 | gratific,positive,bing,2
2632 | gratitudin,positive,bing,2
2633 | grat,positive,bing,2
2634 | gratu,positive,bing,1
2635 | graz,positive,bing,1
2636 | Graz-a-dio,positive,bing,1
2637 | grazios,positive,bing,2
2638 | guadagn,positive,bing,1
2639 | guar,positive,bing,1
2640 | guid,positive,bing,1
2641 | gust,positive,bing,1
2642 | gustos,positive,bing,1
2643 | hah,positive,bing,1
2644 | happ,positive,bing,1
2645 | happy,positive,bing,1
2646 | happy-birthday,positive,bing,1
2647 | hot,positive,bing,1
2648 | i-lov-you,positive,bing,1
2649 | ideal,positive,bing,2
2650 | idealizz,positive,bing,2
2651 | idilliac,positive,bing,3
2652 | idolatr,positive,bing,1
2653 | idol,positive,bing,1
2654 | il-mio-amor,positive,bing,1
2655 | illimit,positive,bing,1
2656 | illumin,positive,bing,1
2657 | illustr,positive,bing,1
2658 | imbatt,positive,bing,1
2659 | immacol,positive,bing,1
2660 | immediat,positive,bing,1
2661 | immens,positive,bing,1
2662 | imparegg,positive,bing,1
2663 | imparzial,positive,bing,1
2664 | imparzialità,positive,bing,1
2665 | impavid,positive,bing,1
2666 | impecc,positive,bing,1
2667 | impeccabil,positive,bing,2
2668 | impegn,positive,bing,1
2669 | imper,positive,bing,1
2670 | imperterr,positive,bing,1
2671 | imperturb,positive,bing,1
2672 | imponent,positive,bing,1
2673 | import,positive,bing,1
2674 | impres,positive,bing,1
2675 | impression,positive,bing,1
2676 | in-mod-sicur,positive,bing,1
2677 | in-omagg,positive,bing,1
2678 | in-particol,positive,bing,1
2679 | inattacc,positive,bing,1
2680 | incant,positive,bing,2
2681 | incantevol,positive,bing,3
2682 | incondizion,positive,bing,1
2683 | incoragg,positive,bing,1
2684 | incred,positive,bing,3
2685 | incredibil,positive,bing,3
2686 | incroll,positive,bing,1
2687 | indenn,positive,bing,1
2688 | indimentic,positive,bing,1
2689 | indiscuss,positive,bing,1
2690 | indiscut,positive,bing,1
2691 | indiscutibil,positive,bing,1
2692 | individualizz,positive,bing,1
2693 | indolor,positive,bing,1
2694 | indulgent,positive,bing,1
2695 | industr,positive,bing,1
2696 | inebr,positive,bing,1
2697 | ineguagl,positive,bing,2
2698 | inequivoc,positive,bing,1
2699 | inequivocabil,positive,bing,1
2700 | inestim,positive,bing,1
2701 | inestimabil,positive,bing,2
2702 | infall,positive,bing,1
2703 | infallibil,positive,bing,1
2704 | influent,positive,bing,1
2705 | influenz,positive,bing,1
2706 | ingegn,positive,bing,1
2707 | ingegnos,positive,bing,1
2708 | ingenu,positive,bing,1
2709 | in1mor,positive,bing,1
2710 | innocent,positive,bing,1
2711 | innocu,positive,bing,1
2712 | innov,positive,bing,1
2713 | inossid,positive,bing,1
2714 | insostitu,positive,bing,1
2715 | intatt,positive,bing,1
2716 | integral,positive,bing,1
2717 | integr,positive,bing,1
2718 | intelligent,positive,bing,2
2719 | intellig,positive,bing,2
2720 | intenzional,positive,bing,1
2721 | interess,positive,bing,1
2722 | intim,positive,bing,1
2723 | intraprendent,positive,bing,2
2724 | intratten,positive,bing,1
2725 | intrattien,positive,bing,1
2726 | intrepid,positive,bing,1
2727 | intric,positive,bing,1
2728 | intrig,positive,bing,1
2729 | intuit,positive,bing,1
2730 | invent,positive,bing,1
2731 | invinc,positive,bing,1
2732 | invincibil,positive,bing,1
2733 | inviol,positive,bing,1
2734 | invulner,positive,bing,1
2735 | ipnotizz,positive,bing,1
2736 | irreal,positive,bing,1
2737 | irreprens,positive,bing,1
2738 | irresist,positive,bing,3
2739 | irresistibil,positive,bing,3
2740 | ispir,positive,bing,1
2741 | istru,positive,bing,1
2742 | istrutt,positive,bing,1
2743 | kiss,positive,bing,1
2744 | labor,positive,bing,1
2745 | lavor-dur,positive,bing,1
2746 | lavor,positive,bing,1
2747 | leal,positive,bing,1
2748 | legal,positive,bing,1
2749 | leggendar,positive,bing,2
2750 | leggerezz,positive,bing,2
2751 | leggiadr,positive,bing,1
2752 | leggibil,positive,bing,1
2753 | legittim,positive,bing,1
2754 | len,positive,bing,1
2755 | liber,positive,bing,1
2756 | libert,positive,bing,1
2757 | lin,positive,bing,1
2758 | lod,positive,bing,1
2759 | lodevol,positive,bing,3
2760 | logic,positive,bing,1
2761 | lol,positive,bing,1
2762 | lov,positive,bing,1
2763 | lov-u,positive,bing,1
2764 | lov-you,positive,bing,1
2765 | low-cost,positive,bing,1
2766 | low-priced,positive,bing,1
2767 | luccic,positive,bing,1
2768 | lucent,positive,bing,2
2769 | lucentezz,positive,bing,2
2770 | lucid,positive,bing,1
2771 | lumin,positive,bing,2
2772 | lusinghier,positive,bing,1
2773 | luss,positive,bing,2
2774 | lussuos,positive,bing,2
2775 | lussuregg,positive,bing,2
2776 | lustr,positive,bing,1
2777 | maest,positive,bing,1
2778 | maestos,positive,bing,3
2779 | maggior,positive,bing,1
2780 | mag,positive,bing,1
2781 | magic,positive,bing,1
2782 | magistral,positive,bing,1
2783 | magnanim,positive,bing,1
2784 | magnif,positive,bing,3
2785 | magnificent,positive,bing,3
2786 | maneggevol,positive,bing,1
2787 | mann,positive,bing,1
2788 | matur,positive,bing,1
2789 | megl,positive,bing,1
2790 | megl-del-previst,positive,bing,1
2791 | memor,positive,bing,1
2792 | Men-stress,positive,bing,1
2793 | meravigl,positive,bing,3
2794 | meritat,positive,bing,3
2795 | meritevol,positive,bing,3
2796 | mer,positive,bing,1
2797 | meritor,positive,bing,1
2798 | meticol,positive,bing,1
2799 | miglior,positive,bing,3
2800 | miglior-performanc,positive,bing,1
2801 | miracol,positive,bing,1
2802 | misericord,positive,bing,1
2803 | mit,positive,bing,1
2804 | mod,positive,bing,1
2805 | modern,positive,bing,2
2806 | modest,positive,bing,1
2807 | molt-acut,positive,bing,1
2808 | molt-ben,positive,bing,1
2809 | monumental,positive,bing,1
2810 | moral,positive,bing,1
2811 | morbid,positive,bing,1
2812 | motiv,positive,bing,1
2813 | moviment,positive,bing,1
2814 | mozzaf,positive,bing,1
2815 | multius,positive,bing,1
2816 | munif,positive,bing,1
2817 | 1vig,positive,bing,1
2818 | nett,positive,bing,1
2819 | nitid,positive,bing,1
2820 | nobil,positive,bing,1
2821 | nobilit,positive,bing,1
2822 | Non-si-applic-restrizion,positive,bing,1
2823 | non-violent,positive,bing,1
2824 | non-violenz,positive,bing,1
2825 | notevol,positive,bing,2
2826 | not,positive,bing,1
2827 | notor,positive,bing,1
2828 | novit,positive,bing,1
2829 | nuov-di-zecc,positive,bing,2
2830 | nutrient,positive,bing,1
2831 | nutr,positive,bing,1
2832 | oas,positive,bing,1
2833 | omagg,positive,bing,1
2834 | onest,positive,bing,1
2835 | onor,positive,bing,1
2836 | onorevol,positive,bing,1
2837 | opportun,positive,bing,1
2838 | opulent,positive,bing,1
2839 | ordi1t,positive,bing,1
2840 | ordin,positive,bing,1
2841 | orecc,positive,bing,1
2842 | orgogl,positive,bing,1
2843 | origi1l,positive,bing,1
2844 | oro,positive,bing,1
2845 | ospital,positive,bing,1
2846 | ottimal,positive,bing,1
2847 | ottim,positive,bing,1
2848 | ovazion,positive,bing,1
2849 | pac,positive,bing,1
2850 | pacif,positive,bing,1
2851 | padron,positive,bing,1
2852 | panoram,positive,bing,1
2853 | paradis,positive,bing,1
2854 | parsimon,positive,bing,1
2855 | passional,positive,bing,1
2856 | passion,positive,bing,2
2857 | patriot,positive,bing,1
2858 | patriott,positive,bing,1
2859 | patt,positive,bing,1
2860 | pazient,positive,bing,1
2861 | pazienz,positive,bing,1
2862 | peacekeeper,positive,bing,1
2863 | pensier,positive,bing,1
2864 | pensos,positive,bing,1
2865 | perd,positive,bing,1
2866 | perfett,positive,bing,3
2867 | perfezion,positive,bing,3
2868 | permett,positive,bing,1
2869 | persever,positive,bing,1
2870 | personalizz,positive,bing,1
2871 | perspicac,positive,bing,1
2872 | piac,positive,bing,2
2873 | piacevol,positive,bing,2
2874 | piacevolezz,positive,bing,2
2875 | pien-di-risors,positive,bing,1
2876 | pien-di-speranz,positive,bing,1
2877 | pittoresc,positive,bing,1
2878 | più-bell,positive,bing,1
2879 | più-cald,positive,bing,1
2880 | più-felic,positive,bing,1
2881 | più-fortun,positive,bing,1
2882 | più-intelligent,positive,bing,1
2883 | più-morbid,positive,bing,1
2884 | più-not,positive,bing,1
2885 | più-pul,positive,bing,1
2886 | più-ricc,positive,bing,1
2887 | più-sald,positive,bing,1
2888 | più-veloc,positive,bing,1
2889 | poeticizz,positive,bing,1
2890 | poetic,positive,bing,1
2891 | popol,positive,bing,1
2892 | port,positive,bing,1
2893 | port-di-man,positive,bing,1
2894 | posit,positive,bing,1
2895 | potent,positive,bing,1
2896 | predilezion,positive,bing,1
2897 | prefer,positive,bing,1
2898 | preferibil,positive,bing,1
2899 | prem,positive,bing,1
2900 | preminent,positive,bing,1
2901 | premur,positive,bing,1
2902 | prestig,positive,bing,2
2903 | prezios,positive,bing,2
2904 | primar,positive,bing,1
2905 | Prim-in-class,positive,bing,1
2906 | privileg,positive,bing,1
2907 | priv-di-risc,positive,bing,1
2908 | prodezz,positive,bing,1
2909 | prodig,positive,bing,1
2910 | produtt,positive,bing,1
2911 | profession,positive,bing,2
2912 | profond,positive,bing,1
2913 | profumat,positive,bing,1
2914 | profusion,positive,bing,1
2915 | progress,positive,bing,1
2916 | prolif,positive,bing,1
2917 | promess,positive,bing,1
2918 | promettent,positive,bing,2
2919 | prominent,positive,bing,1
2920 | promotor,positive,bing,1
2921 | pront,positive,bing,1
2922 | propiziator,positive,bing,1
2923 | propiz,positive,bing,1
2924 | prosper,positive,bing,1
2925 | protegg,positive,bing,1
2926 | protett,positive,bing,1
2927 | protezion,positive,bing,1
2928 | prov,positive,bing,1
2929 | provvident,positive,bing,1
2930 | prudent,positive,bing,1
2931 | prudenz,positive,bing,1
2932 | pul,positive,bing,1
2933 | puliz,positive,bing,1
2934 | puntual,positive,bing,1
2935 | purific,positive,bing,1
2936 | pur,positive,bing,1
2937 | qualific,positive,bing,1
2938 | qui,positive,bing,1
2939 | raccomand,positive,bing,2
2940 | raddrizz,positive,bing,1
2941 | raffin,positive,bing,1
2942 | raffinatezz,positive,bing,2
2943 | raggiant,positive,bing,1
2944 | raggiung,positive,bing,1
2945 | ragionevol,positive,bing,2
2946 | rallegr,positive,bing,1
2947 | rapid-cresc,positive,bing,1
2948 | rapid,positive,bing,1
2949 | rapport,positive,bing,1
2950 | rassicur,positive,bing,1
2951 | ravviv,positive,bing,1
2952 | razional,positive,bing,1
2953 | realizz,positive,bing,1
2954 | recuper,positive,bing,1
2955 | redentr,positive,bing,1
2956 | regal,positive,bing,1
2957 | registr-lavor,positive,bing,1
2958 | regol,positive,bing,1
2959 | resistent,positive,bing,1
2960 | responsabilizz,positive,bing,1
2961 | responsabil,positive,bing,1
2962 | restaur,positive,bing,1
2963 | retribu,positive,bing,1
2964 | rett,positive,bing,1
2965 | riafferm,positive,bing,1
2966 | rianim,positive,bing,1
2967 | ricc,positive,bing,2
2968 | ricchezz,positive,bing,1
2969 | ricc-di-funzio1l,positive,bing,1
2970 | ricett,positive,bing,1
2971 | ricompens,positive,bing,1
2972 | riconcil,positive,bing,1
2973 | riconduc,positive,bing,1
2974 | riconoscent,positive,bing,1
2975 | riconosc,positive,bing,1
2976 | riduzion-dell-stress,positive,bing,1
2977 | riesc,positive,bing,1
2978 | rid,positive,bing,1
2979 | riform,positive,bing,1
2980 | rigener,positive,bing,1
2981 | riguard,positive,bing,1
2982 | rilass,positive,bing,1
2983 | rimbors,positive,bing,1
2984 | rimed,positive,bing,1
2985 | ri1sc,positive,bing,1
2986 | rinfranc,positive,bing,1
2987 | rinfresc,positive,bing,1
2988 | ringiovan,positive,bing,1
2989 | ringraz,positive,bing,1
2990 | rinnov,positive,bing,1
2991 | rinvigor,positive,bing,1
2992 | ripos,positive,bing,1
2993 | riscatt,positive,bing,1
2994 | risolutezz,positive,bing,1
2995 | risol,positive,bing,1
2996 | risp,positive,bing,1
2997 | risparm,positive,bing,2
2998 | risparm-di-cost,positive,bing,1
2999 | risparm-energet,positive,bing,1
3000 | rispett,positive,bing,1
3001 | rispett-dell-legg,positive,bing,1
3002 | risplendent,positive,bing,1
3003 | ristruttur,positive,bing,1
3004 | riusc,positive,bing,1
3005 | rivel,positive,bing,1
3006 | riverent,positive,bing,1
3007 | river,positive,bing,1
3008 | riviv,positive,bing,1
3009 | rivoluzion,positive,bing,1
3010 | rivoluzionar,positive,bing,1
3011 | robust,positive,bing,1
3012 | romant,positive,bing,2
3013 | rose,positive,bing,1
3014 | sagac,positive,bing,1
3015 | saggement,positive,bing,1
3016 | saggezz,positive,bing,1
3017 | sagg,positive,bing,1
3018 | salubr,positive,bing,1
3019 | salut,positive,bing,2
3020 | sal,positive,bing,1
3021 | salvator,positive,bing,1
3022 | salvav,positive,bing,1
3023 | san,positive,bing,1
3024 | san-di-ment,positive,bing,1
3025 | santific,positive,bing,1
3026 | santit,positive,bing,1
3027 | sant,positive,bing,1
3028 | sapient,positive,bing,1
3029 | sbalordit,positive,bing,1
3030 | sbalord,positive,bing,1
3031 | scenic,positive,bing,1
3032 | sciccos,positive,bing,1
3033 | scintill,positive,bing,1
3034 | sconfigg,positive,bing,1
3035 | sconfitt,positive,bing,1
3036 | scopert,positive,bing,1
3037 | semplic,positive,bing,1
3038 | semplif,positive,bing,1
3039 | semplific,positive,bing,1
3040 | sensazional,positive,bing,2
3041 | sensazion,positive,bing,1
3042 | sensibil,positive,bing,1
3043 | sentimental,positive,bing,1
3044 | senz-dolor,positive,bing,1
3045 | senz-dubb,positive,bing,1
3046 | senz-paur,positive,bing,1
3047 | senz-problem,positive,bing,1
3048 | senz-restrizion,positive,bing,1
3049 | senz-rival,positive,bing,1
3050 | senz-sforz,positive,bing,1
3051 | seren,positive,bing,1
3052 | seriet,positive,bing,1
3053 | sexy,positive,bing,1
3054 | sfarz,positive,bing,1
3055 | sicur,positive,bing,1
3056 | signif,positive,bing,1
3057 | simpat,positive,bing,2
3058 | sincer,positive,bing,2
3059 | soav,positive,bing,2
3060 | soavement,positive,bing,2
3061 | socievol,positive,bing,2
3062 | soddisf,positive,bing,2
3063 | soddisfacent,positive,bing,2
3064 | soddisfatt,positive,bing,2
3065 | solid,positive,bing,1
3066 | solidariet,positive,bing,2
3067 | sollet,positive,bing,1
3068 | sollev,positive,bing,1
3069 | soll,positive,bing,1
3070 | sonor,positive,bing,1
3071 | sontuos,positive,bing,1
3072 | sorprendent,positive,bing,3
3073 | sorrid,positive,bing,2
3074 | sorrident,positive,bing,2
3075 | sorris,positive,bing,2
3076 | sostanzial,positive,bing,1
3077 | sostegn,positive,bing,1
3078 | sosten,positive,bing,1
3079 | sostenibil,positive,bing,1
3080 | sostenitor,positive,bing,1
3081 | sovraperform,positive,bing,1
3082 | sovvenzion,positive,bing,1
3083 | spazios,positive,bing,2
3084 | specializz,positive,bing,1
3085 | spensier,positive,bing,1
3086 | spettacol,positive,bing,2
3087 | spin-dorsal,positive,bing,1
3088 | spint,positive,bing,1
3089 | spirit,positive,bing,1
3090 | spiritual,positive,bing,1
3091 | splendid,positive,bing,3
3092 | splendor,positive,bing,3
3093 | spontane,positive,bing,1
3094 | sport,positive,bing,1
3095 | squisit,positive,bing,3
3096 | squis,positive,bing,1
3097 | stabil,positive,bing,1
3098 | stabilizz,positive,bing,1
3099 | staccabil,positive,bing,1
3100 | stagion,positive,bing,1
3101 | statuar,positive,bing,1
3102 | stell,positive,bing,1
3103 | stilizz,positive,bing,1
3104 | stimol,positive,bing,1
3105 | stord,positive,bing,1
3106 | straordinar,positive,bing,3
3107 | strumental,positive,bing,1
3108 | stupend,positive,bing,3
3109 | stup,positive,bing,1
3110 | stupor,positive,bing,1
3111 | stuzzic,positive,bing,2
3112 | sublim,positive,bing,3
3113 | success,positive,bing,2
3114 | successon,positive,bing,2
3115 | sufficient,positive,bing,1
3116 | super,positive,bing,3
3117 | super-in-astuz,positive,bing,1
3118 | superb,positive,bing,3
3119 | superior,positive,bing,2
3120 | superst,positive,bing,1
3121 | support,positive,bing,1
3122 | suprem,positive,bing,1
3123 | supremaz,positive,bing,1
3124 | surreal,positive,bing,1
3125 | svolazz,positive,bing,1
3126 | talent,positive,bing,1
3127 | talentu,positive,bing,1
3128 | tant-megl,positive,bing,1
3129 | tempest,positive,bing,1
3130 | temp-onor,positive,bing,1
3131 | tenac,positive,bing,1
3132 | tener,positive,bing,1
3133 | tent,positive,bing,1
3134 | tesor,positive,bing,1
3135 | titill,positive,bing,1
3136 | toccasan,positive,bing,1
3137 | toller,positive,bing,1
3138 | tonific,positive,bing,1
3139 | ton-rassicur,positive,bing,1
3140 | top,positive,bing,1
3141 | tranquill,positive,bing,1
3142 | trasparent,positive,bing,1
3143 | tregu,positive,bing,1
3144 | tremend,positive,bing,1
3145 | trionfal,positive,bing,1
3146 | trionfant,positive,bing,1
3147 | trionf,positive,bing,1
3148 | trofe,positive,bing,1
3149 | tutt-il-suo-fascin,positive,bing,1
3150 | tutt-tond,positive,bing,1
3151 | ultra-nitid,positive,bing,1
3152 | uman,positive,bing,1
3153 | umil,positive,bing,1
3154 | umilt,positive,bing,1
3155 | umor,positive,bing,1
3156 | umorist,positive,bing,1
3157 | unit,positive,bing,1
3158 | urrà,positive,bing,1
3159 | user-friendly,positive,bing,1
3160 | util,positive,bing,1
3161 | utilizz,positive,bing,1
3162 | val-la-pen,positive,bing,1
3163 | valent,positive,bing,1
3164 | valor,positive,bing,1
3165 | vantagg,positive,bing,1
3166 | varieg,positive,bing,1
3167 | variet,positive,bing,1
3168 | veloc,positive,bing,1
3169 | vener,positive,bing,1
3170 | verac,positive,bing,1
3171 | verific,positive,bing,1
3172 | veritier,positive,bing,1
3173 | ver,positive,bing,1
3174 | vers,positive,bing,1
3175 | versatil,positive,bing,1
3176 | vibrant,positive,bing,1
3177 | vigil,positive,bing,1
3178 | vigor,positive,bing,1
3179 | vinc,positive,bing,1
3180 | vincitor,positive,bing,2
3181 | virtuos,positive,bing,2
3182 | vistos,positive,bing,1
3183 | vittor,positive,bing,1
3184 | vivac,positive,bing,1
3185 | vivid,positive,bing,1
3186 | volentier,positive,bing,2
3187 | zel,positive,bing,1
3188 | zenit,positive,bing,1
3189 | emotegood,positive,bing,1
3190 | emotelov,positive,bing,1
3191 | emotewink,positive,bing,1
3192 | emotekiss,positive,bing,1
3193 | emotewow,positive,bing,1
3194 | emotelol,positive,bing,1
3195 | emoteeheh,positive,bing,1
3196 | emoteah,positive,bing,1


--------------------------------------------------------------------------------
/ScrapeAdvisor.py:
--------------------------------------------------------------------------------
  1 | import glob
  2 | import os
  3 | import subprocess
  4 | import threading
  5 | import time
  6 | import kivy
  7 | import logging
  8 | 
  9 | from kivy.app import App
 10 | from kivy.lang import Builder
 11 | from kivy.uix.boxlayout import BoxLayout
 12 | from kivy.uix.image import Image
 13 | from kivy.uix.label import Label
 14 | from CarouselClass import CarouselApp
 15 | from ScraperClass import ScraperClass
 16 | 
 17 | kivy.require("1.9.0")
 18 | 
 19 | kv = """    
 20 | <Row@BoxLayout>:
 21 |     canvas.before:
 22 |         Color:
 23 |             rgba: 0.5, 0.5, 0.5, 1
 24 |         Rectangle:
 25 |             size: self.size
 26 |             pos: self.pos
 27 |     value: ''
 28 |     ScrollView:
 29 |         size: self.size
 30 |         Label:
 31 |             padding_x: dp(10)
 32 |             padding_y: dp(10)
 33 |             text: root.value
 34 |             #halign: 'left'
 35 |             #valign: 'top'
 36 |             size_hint_y: None
 37 |             text_size: self.width, None
 38 |             height: self.texture_size[1]
 39 |             markup: True
 40 | <Test>:
 41 |     canvas:
 42 |         Color:
 43 |             rgba: 0.3, 0.3, 0.3, 1
 44 |         Rectangle:
 45 |             size: self.size
 46 |             pos: self.pos
 47 |     rv: rv
 48 |     orientation: 'vertical'
 49 |     GridLayout:
 50 |         cols: 2
 51 |         rows: 1
 52 |         size_hint_y: None
 53 |         height: dp(50)
 54 |         padding: dp(8)
 55 |         spacing: dp(16)
 56 |         TextInput:
 57 |             id: input_url
 58 |             multiline: False
 59 |             size_hint_x: 6
 60 |             hint_text: 'http://www.tripadvisor.com/'
 61 |             padding: dp(10), dp(10), 0, 0
 62 |             on_text_validate: root.thread_rev(input_url.text)
 63 |         Button:
 64 |             text: 'Enter'
 65 |             on_press: root.thread_rev(input_url.text)
 66 |     GridLayout:
 67 |         cols: 3
 68 |         rows: 1
 69 |         size_hint_y: None
 70 |         height: dp(70)
 71 |         padding: dp(8)
 72 |         spacing: dp(16)
 73 |         rv: rv.__self__
 74 |         Button:
 75 |             text: 'Show Reviews'
 76 |             on_press: root.show_review()
 77 |         Button:
 78 |             text: 'Sentiment Analysis'
 79 |             on_press: root.thread_sent()
 80 |         Button:
 81 |             text: 'About'
 82 |             on_press: root.about()
 83 |     RecycleView:
 84 |         id: rv
 85 |         scroll_type: ['bars', 'content']
 86 |         scroll_wheel_distance: dp(114)
 87 |         bar_width: dp(20)
 88 |         viewclass: 'Row'
 89 |         RecycleBoxLayout:
 90 |             padding: dp(30)
 91 |             default_size: None, dp(200)
 92 |             default_size_hint: 1, None
 93 |             size_hint_y: None
 94 |             height: self.minimum_height
 95 |             orientation: 'vertical'
 96 |             spacing: dp(10)
 97 | """
 98 | 
 99 | Builder.load_string(kv)
100 | 
101 | logging.basicConfig(level=logging.INFO)
102 | logger = logging.getLogger(__name__)
103 | 
104 | 
105 | class Test(BoxLayout):
106 | 
107 |     def __init__(self, **kwargs):
108 |         super().__init__(**kwargs)
109 |         self.label = Label(markup=True)
110 |         self.img = Image(source='res/about.png')
111 |         self.carousel = CarouselApp().build()
112 |         self.add_widget(self.label)
113 |         self.is_info_visible = True
114 |         self.is_rv_visible = True
115 |         self.is_img_visible = False
116 |         self.is_car_visible = False
117 |         self.new_url = False
118 |         self.scraper = None
119 |         self.lang = ""
120 | 
121 |     # thread for downloading the reviews
122 |     def thread_rev(self, input_url):
123 |         # removes older reviews loaded
124 |         if self.rv.data:
125 |             self.rv.data = []
126 | 
127 |         self.new_url = False
128 | 
129 |         # check for displayed labels/widget
130 |         if self.is_rv_visible:
131 |             self.remove_widget(self.ids.rv)
132 |             self.is_rv_visible = False
133 | 
134 |         if self.is_car_visible:
135 |             self.remove_widget(self.carousel)
136 |             self.is_car_visible = False
137 | 
138 |         if self.is_img_visible:
139 |             self.remove_widget(self.img)
140 |             self.is_img_visible = False
141 | 
142 |         # remove all csv files
143 |         for f in glob.glob("*.csv"):
144 |             os.remove(f)
145 | 
146 |         # get language
147 |         self.get_lang(input_url)
148 | 
149 |         # and the content of prev R plot
150 |         files = glob.glob('Routput/*')
151 |         for f in files:
152 |             os.remove(f)
153 | 
154 |         if self.is_info_visible:
155 |             self.label.text = '[size=40][b][color=ffffff]Loading reviews[/color][/b][/size]'
156 |         else:
157 |             self.add_widget(self.label)
158 |             self.label.text = '[size=40][b][color=ffffff]Loading reviews[/color][/b][/size]'
159 |             self.is_info_visible = True
160 | 
161 |         # start thread for downloading reviews
162 |         thread = threading.Thread(target=self.calculate, args=(input_url,))
163 |         logger.info("Input url: " + input_url)
164 |         thread.daemon = True
165 |         thread.start()
166 |         logger.info("[*] Downloading thread started")
167 | 
168 |     # thread for sentiment analysis
169 |     def thread_sent(self):
170 |         thread = threading.Thread(target=self.sentiment)
171 |         thread.daemon = True
172 |         thread.start()
173 | 
174 |     # function called when "Equals" is pressed
175 |     def calculate(self, calculation):
176 |         try:
177 |             self.scraper = ScraperClass(calculation)
178 |             self.scraper.get_review()
179 |             self.label.text = '[size=40][b][color=#4caf50]Reviews loaded[/color][/b][/size]'
180 | 
181 |         except Exception as e:
182 |             logger.error(e)
183 |             logger.error("[X] Invalid URL")
184 |             time.sleep(1)
185 |             self.label.text = '[size=40][b][color=f44336]Invalid URL[/color][/b][/size]'
186 | 
187 |     # function called when "Show Reviews" is pressed
188 |     def show_review(self):
189 |         try:
190 |             if self.is_info_visible:
191 |                 self.remove_widget(self.label)
192 |                 self.is_info_visible = False
193 | 
194 |             if self.is_car_visible:
195 |                 self.remove_widget(self.carousel)
196 |                 self.is_car_visible = False
197 | 
198 |             if self.is_img_visible:
199 |                 self.remove_widget(self.img)
200 |                 self.is_img_visible = False
201 | 
202 |             if not self.is_rv_visible:
203 |                 self.add_widget(self.ids.rv)
204 |                 self.is_rv_visible = True
205 | 
206 |             self.rv.data = [{'value': '[size=17][b]' + "Title: " + '[/b][/size]' + x[0][
207 |                 'review_title'].lower().capitalize() + "\n" +
208 |                                       '[size=17][b]' + "Review: " + '[/b][/size]' + x[0][
209 |                                           'review_body'].lower().capitalize() + "\n" +
210 |                                       '[size=17][b]' + "Date: " + '[/b][/size]' + x[0]['review_date'] + "\n" +
211 |                                       '[size=17][b]' + "User Location: " + '[/b][/size]' + x[0][
212 |                                           'user_location'] + "\n" +
213 |                                       '[size=17][b]' + "Rating: " + '[/b][/size]' + x[0]['rating'] + "\n" +
214 |                                       '[size=17][b]' + "Contribution: " + '[/b][/size]' + x[0]['contributions'] + "\n" +
215 |                                       '[size=17][b]' + "Helpful Vote: " + '[/b][/size]' + x[0]['helpful_vote']} for x in
216 |                             self.scraper.get_item()]
217 | 
218 |             if not self.rv.data:
219 |                 self.remove_widget(self.ids.rv)
220 |                 self.is_rv_visible = False
221 |                 if self.is_info_visible:
222 |                     self.label.text = '[size=40][b][color=f44336]No data to load[/color][/b][/size]'
223 |                 else:
224 |                     self.add_widget(self.label)
225 |                     self.label.text = '[size=40][b][color=f44336]No data to load[/color][/b][/size]'
226 |                     self.is_info_visible = True
227 | 
228 |         except Exception as e:
229 |             logger.error(e)
230 |             self.remove_widget(self.ids.rv)
231 |             self.is_rv_visible = False
232 |             logger.error("[X] No data to load")
233 |             if self.is_info_visible:
234 |                 self.label.text = '[size=40][b][color=f44336]No data to load[/color][/b][/size]'
235 |             else:
236 |                 self.add_widget(self.label)
237 |                 self.label.text = '[size=40][b][color=f44336]No data to load[/color][/b][/size]'
238 |                 self.is_info_visible = True
239 | 
240 |     # function called when "Sentiment Analysis" is pressed
241 |     def sentiment(self):
242 |         try:
243 |             # when the url provided is not changed
244 |             if self.new_url:
245 |                 if self.is_rv_visible:
246 |                     self.remove_widget(self.ids.rv)
247 |                     self.is_rv_visible = False
248 | 
249 |                 if self.is_img_visible:
250 |                     self.remove_widget(self.img)
251 |                     self.is_img_visible = False
252 | 
253 |                 if not self.is_car_visible:
254 |                     self.add_widget(self.carousel)
255 |                     self.is_car_visible = True
256 | 
257 |             # the url is new
258 |             else:
259 |                 # removes older reviews loaded
260 |                 if self.is_rv_visible:
261 |                     self.remove_widget(self.ids.rv)
262 |                     self.is_rv_visible = False
263 | 
264 |                 if self.is_car_visible:
265 |                     self.remove_widget(self.carousel)
266 |                     self.is_car_visible = False
267 | 
268 |                 if self.is_img_visible:
269 |                     self.remove_widget(self.img)
270 |                     self.is_img_visible = False
271 | 
272 |                 if self.is_info_visible:
273 |                     self.label.text = '[size=40][b][color=ffffff]Running R sentiment analysis[/color][/b][/size]'
274 |                     time.sleep(1)
275 |                 else:
276 |                     self.add_widget(self.label)
277 |                     self.label.text = '[size=40][b][color=ffffff]Running R sentiment analysis[/color][/b][/size]'
278 |                     time.sleep(1)
279 |                     self.is_info_visible = True
280 | 
281 |                 # checks if there's a csv file somewhere
282 |                 if self.scraper.get_filename():
283 |                     try:
284 |                         subprocess.call(
285 |                             ["C:/Program Files/R/R-3.5.3/bin/x64/Rscript.exe", "Rinput/" + self.lang + ".R",
286 |                              self.scraper.get_filename() + ".csv"],
287 |                             shell=True)
288 |                         self.label.text = '[size=40][b][color=#4caf50]Done[/color][/b][/size]'
289 |                         time.sleep(1)
290 |                         self.remove_widget(self.ids.rv)
291 | 
292 |                         # create Carousel object and display it
293 |                         if not self.is_car_visible:
294 |                             self.carousel = CarouselApp().build()
295 |                             self.remove_widget(self.label)
296 |                             self.is_info_visible = False
297 |                             self.add_widget(self.carousel)
298 |                             self.is_car_visible = True
299 |                             self.new_url = True
300 | 
301 |                     except Exception as e:
302 |                         logger.error(e)
303 |                         if self.is_info_visible:
304 |                             self.label.text = '[size=40][b][color=f44336]No CSV file found[/color][/b][/size]'
305 |                         else:
306 |                             self.add_widget(self.label)
307 |                             self.label.text = '[size=40][b][color=f44336]No CSV file found[/color][/b][/size]'
308 |                             self.is_info_visible = True
309 |                         logger.error("[X] No CSV file found")
310 |                 else:
311 |                     self.label.text = '[size=40][b][color=f44336]No CSV file found[/color][/b][/size]'
312 |                     logger.error("[X] No CSV file found")
313 | 
314 |         except Exception as e:
315 |             logger.error(e)
316 |             self.label.text = '[size=40][b][color=f44336]No CSV file found[/color][/b][/size]'
317 |             logger.error("[X] No CSV file found")
318 | 
319 |     # function called when "About" is pressed
320 |     def about(self):
321 |         # check for displayed labels/widget
322 |         if self.is_rv_visible:
323 |             self.remove_widget(self.ids.rv)
324 |             self.is_rv_visible = False
325 | 
326 |         if self.is_car_visible:
327 |             self.remove_widget(self.carousel)
328 |             self.is_car_visible = False
329 | 
330 |         if self.is_info_visible:
331 |             self.remove_widget(self.label)
332 |             self.is_info_visible = False
333 | 
334 |         if not self.is_img_visible:
335 |             self.add_widget(self.img)
336 |             self.is_img_visible = True
337 | 
338 |     # get language of reviews
339 |     def get_lang(self, domain):
340 |         if "www.tripadvisor.it" in domain:
341 |             self.lang = "it"
342 | 
343 |         elif "www.tripadvisor.com" in domain:
344 |             self.lang = "en"
345 | 
346 |         else:
347 |             logger.error("[X] Language not supported")
348 | 
349 | 
350 | # main class
351 | class ScrapeAdvisor(App):
352 |     def build(self):
353 |         return Test()
354 | 
355 | 
356 | # run baby run run
357 | if __name__ == '__main__':
358 |     ScrapeAdvisor().run()
359 | 


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/ScraperClass.py:
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  1 | import io
  2 | import webbrowser
  3 | import requests
  4 | import unicodecsv as csv
  5 | import logging
  6 | from bs4 import BeautifulSoup
  7 | 
  8 | DB_COLUMN = 'review_title'
  9 | DB_COLUMN1 = 'review_body'
 10 | DB_COLUMN2 = 'review_date'
 11 | DB_COLUMN3 = 'contributions'
 12 | DB_COLUMN4 = 'helpful_vote'
 13 | DB_COLUMN5 = 'user_location'
 14 | DB_COLUMN6 = 'rating'
 15 | 
 16 | logging.basicConfig(level=logging.INFO)
 17 | logger = logging.getLogger(__name__)
 18 | 
 19 | 
 20 | class ScraperClass:
 21 |     def __init__(self, url):
 22 |         self.headers = [
 23 |             DB_COLUMN,
 24 |             DB_COLUMN1,
 25 |             DB_COLUMN2,
 26 |             DB_COLUMN3,
 27 |             DB_COLUMN4,
 28 |             DB_COLUMN5,
 29 |             DB_COLUMN6,
 30 |         ]
 31 |         self.start_url = [url]
 32 |         self.lang = 'it'
 33 |         # self.get_review()
 34 |         self.item_list = list()
 35 |         self.dict = dict()
 36 |         self.filename = ""
 37 | 
 38 |     def get_review(self):
 39 |         for url in self.start_url:
 40 |             items = self.scrape(url, self.lang)
 41 |             if not items:
 42 |                 logger.info('No reviews')
 43 |             else:
 44 |                 # write in CSV
 45 |                 filename = url.split('Reviews-')[1][:-5] + '__' + self.lang
 46 |                 logger.info('filename:', filename)
 47 |                 self.filename = filename
 48 |                 self.write_in_csv(items, filename + '.csv', self.headers, mode='wb')
 49 | 
 50 |     def scrape(self, url, lang):
 51 |         session = requests.Session()
 52 |         session.headers.update({
 53 |             'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:57.0) Gecko/20100101 Firefox/57.0',
 54 |         })
 55 |         items = self.parse(session, url + '?filterLang=' + lang)
 56 |         return items
 57 | 
 58 |     def parse(self, session, url):
 59 |         # get number of reviews and start getting sub-pages with reviews
 60 |         logger.info('[parse] url:', url)
 61 |         soup = self.get_soup(session, url)
 62 |         if not soup:
 63 |             logger.error('[parse] no soup:', url)
 64 |             return
 65 |         num_reviews = soup.find('span', class_='reviews_header_count').text  # get text
 66 |         num_reviews = num_reviews[1:-1]
 67 |         num_reviews = num_reviews.replace(',', '')
 68 |         num_reviews = int(num_reviews)  # convert text into integer
 69 |         logger.info('[parse] num_reviews ALL:', num_reviews)
 70 |         url_template = url.replace('.html', '-or{}.html')
 71 |         logger.info('[parse] url_template:', url_template)
 72 |         items = []
 73 |         offset = 0
 74 | 
 75 |         while True:
 76 |             subpage_url = url_template.format(offset)
 77 |             subpage_items = self.parse_reviews(session, subpage_url)
 78 |             if not subpage_items:
 79 |                 break
 80 |             items += subpage_items
 81 |             if len(subpage_items) < 10:
 82 |                 break
 83 |             offset += 10
 84 |         return items
 85 | 
 86 |     def get_soup(self, session, url, show=False):
 87 |         r = session.get(url)
 88 |         if show:
 89 |             self.display(r.content, 'temp.html')
 90 |         if r.status_code != 200:  # not OK
 91 |             logger.info('[get_soup] status code:', r.status_code)
 92 |         else:
 93 |             return BeautifulSoup(r.text, 'html.parser')
 94 | 
 95 |     def post_soup(self, session, url, params, show=False):
 96 |         # read HTML from server and convert to Soup
 97 |         r = session.post(url, data=params)
 98 |         if show:
 99 |             self.display(r.content, 'temp.html')
100 |         if r.status_code != 200:  # not OK
101 |             logger.error('[post_soup] status code:', r.status_code)
102 |         else:
103 |             return BeautifulSoup(r.text, 'html.parser')
104 | 
105 |     def display(self, content, filename='output.html'):
106 |         with open(filename, 'wb') as f:
107 |             f.write(content)
108 |             webbrowser.open(filename)
109 | 
110 |     def parse_reviews(self, session, url):
111 |         # get all reviews from one page
112 |         logger.info('[parse_reviews] url:', url)
113 |         soup = self.get_soup(session, url)
114 | 
115 |         if not soup:
116 |             logger.error('[parse_reviews] no soup:', url)
117 |             return
118 | 
119 |         facility_name = soup.find('h1', class_='header heading masthead masthead_h1').text
120 |         reviews_ids = self.get_reviews_ids(soup)
121 |         if not reviews_ids:
122 |             return
123 | 
124 |         soup = self.get_more(session, reviews_ids)
125 |         if not soup:
126 |             logger.error('[parse_reviews] no soup:', url)
127 |             return
128 | 
129 |         items = []
130 |         for idx, review in enumerate(soup.find_all('div', class_='reviewSelector')):
131 |             badgets = review.find_all('span', class_='badgetext')
132 | 
133 |             if len(badgets) > 0:
134 |                 contributions = badgets[0].text
135 |             else:
136 |                 contributions = '0'
137 | 
138 |             if len(badgets) > 1:
139 |                 helpful_vote = badgets[1].text
140 |             else:
141 |                 helpful_vote = '0'
142 |             user_loc = review.select_one('div.userLoc strong')
143 | 
144 |             if user_loc:
145 |                 splitted = user_loc.text.split(",")  # we want to delete regions like "Pavia, Lombardia, Italia"
146 |                 if len(splitted) > 2:
147 |                     user_loc = splitted[0] + "," + splitted[2]  # taking only city and state
148 |                 else:
149 |                     user_loc = user_loc.text
150 |             else:
151 |                 user_loc = 'NA'
152 |             bubble_rating = review.select_one('span.ui_bubble_rating')['class']
153 |             bubble_rating = bubble_rating[1].split('_')[-1][-2]
154 |             item = {
155 |                 'review_title': review.find('span', class_='noQuotes').text,
156 |                 'review_body': review.find('p', class_='partial_entry').text,
157 |                 'review_date': review.find('span', class_='ratingDate')['title'],
158 |                 'contributions': contributions,
159 |                 'helpful_vote': helpful_vote,
160 |                 'user_location': user_loc,
161 |                 'rating': bubble_rating
162 |             }
163 | 
164 |             items.append(item)
165 |             logger.info('\n--- review ---\n')
166 |             dict_item = dict()
167 |             for key, val in item.items():
168 |                 logger.info(' ', key, ':', val)
169 |                 dict_item[key] = val
170 |             self.item_list.append([dict_item])
171 |         return items
172 | 
173 |     def get_filename(self):
174 |         return self.filename
175 | 
176 |     def get_reviews_ids(self, soup):
177 |         items = soup.find_all('div', attrs={'data-reviewid': True})
178 |         if items:
179 |             reviews_ids = [x.attrs['data-reviewid'] for x in items][::2]
180 |             logger.info('[get_reviews_ids] data-reviewid:', reviews_ids)
181 |             return reviews_ids
182 | 
183 |     def get_more(self, session, reviews_ids):
184 |         url = 'https://www.tripadvisor.com/OverlayWidgetAjax?Mode=EXPANDED_HOTEL_REVIEWS_RESP&metaReferer=Hotel_Review'
185 |         payload = {
186 |             'reviews': ','.join(reviews_ids),  # ie. "577882734,577547902,577300887",
187 |             'widgetChoice': 'EXPANDED_HOTEL_REVIEW_HSX',
188 |             'haveJses': 'earlyRequireDefine,amdearly,global_error,long_lived_global,apg-Hotel_Review,'
189 |                         'apg-Hotel_Review-in,bootstrap,desktop-rooms-guests-dust-en_US,'
190 |                         'responsive-calendar-templates-dust-en_US,taevents',
191 |             'haveCsses': 'apg-Hotel_Review-in',
192 |             'Action': 'install',
193 |         }
194 |         soup = self.post_soup(session, url, payload)
195 |         return soup
196 | 
197 |     def write_in_csv(self, items, filename='results.csv',
198 |                      headers=None,
199 |                      mode='w'):
200 |         if headers is None:
201 |             headers = ['hotel name', 'review title', 'review body',
202 |                        'review date', 'contributions', 'helpful vote',
203 |                        'user name', 'user location', 'rating']
204 |         logger.info('--- CSV ---')
205 |         with io.open(filename, mode) as csvfile:
206 |             csv_file = csv.DictWriter(csvfile, headers, encoding='utf-8')
207 |             if mode == 'wb':
208 |                 csv_file.writeheader()
209 |             csv_file.writerows(items)
210 | 
211 |     def get_item(self):
212 |         return self.item_list
213 | 
214 | # TrpAdvScrp = ScraperClass('http://www.tripadvisor..')
215 | # TrpAdvScrp.get_review()
216 | 


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/ipak.R:
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 1 | # ipak function: install and load multiple R packages.
 2 | # check to see if packages are installed. Install them if they are not, then load them into the R session.
 3 | 
 4 | ipak <- function(pkg){
 5 |     new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
 6 |     if (length(new.pkg)) 
 7 |         install.packages(new.pkg, dependencies = TRUE)
 8 |     sapply(pkg, require, character.only = TRUE)
 9 | }
10 | 
11 | # usage
12 | packages <- c("dplyr","readr","lubridate","ggplot2","tidytext","tidyverse","stringr","tidyr","scales","broom","purrr",
13 | 				"widyr","igraph","ggraph","SnowballC","wordcloud","reshape2","TeachingDemos")
14 | ipak(packages)


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/requirements.txt:
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1 | requests == 2.22.0
2 | kivy.deps.sdl2==0.1.18
3 | unicodecsv==0.14.1
4 | Pillow==6.1.0
5 | beautifulsoup4==4.7.1
6 | kivy==1.11.1
7 | 


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/res/about.png:
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https://raw.githubusercontent.com/0x0be/scrapeadvisor/fac295ff99e2428e7ce8c8875f1bd427bb101942/res/about.png


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