import random
from urllib.request import urlopen

import collections
from bs4 import BeautifulSoup
import pandas as pd
import time
import nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from textblob import Word
from textblob import TextBlob
import string
import re
import nltk
from nltk.corpus import stopwords
Scraping reviews

####
base_url = "https://www.tripadvisor.com"

hotel_url = base_url + "/Restaurant_Review-g297701-d4552317-Reviews-Luxe_Barbeque_and_Grill-Ubud_Gianyar_Regency_Bali.html#REVIEWS"
html = urlopen(hotel_url)
soup = BeautifulSoup(html, 'html.parser')
reviews_div = soup.find_all("div",class_="rev_wrap ui_columns is-multiline")
####
total_review = 0
df = pd.DataFrame(columns=['Rating', "Review"])
for review in reviews_div:
rating = 1
r = review.find("span", class_="ui_bubble_rating bubble_50")
if r is not None:
   rating = 5
r = review.find("span", class_="ui_bubble_rating bubble_45")
if r is not None:
   rating = 4.5
r = review.find("span", class_="ui_bubble_rating bubble_40")
if r is not None:
   rating = 4
r = review.find("span", class_="ui_bubble_rating bubble_35")
if r is not None:
   rating = 3.5
r = review.find("span", class_="ui_bubble_rating bubble_30")
if r is not None:
   rating = 3
r = review.find("span", class_="ui_bubble_rating bubble_25")
if r is not None:
   rating = 2.5
r = review.find("span", class_="ui_bubble_rating bubble_20")
if r is not None:
   rating = 2
r = review.find("span", class_="ui_bubble_rating bubble_15")
if r is not None:
   rating = 1.5
r = review.find("span", class_="ui_bubble_rating bubble_10")
if r is not None:
   rating = 1

r_text_cont = review.find("div",
                           class_="entry")

r_text = r_text_cont.find("p").text
df = df.append({'Rating': rating, 'Review': r_text}, ignore_index=True)
total_review +=1

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