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from autoscraper import AutoScraper
# replace with desired url
url = 'https://www.yelp.com/biz/chun-yang-tea-flushing-new-york-flushing' 
# make sure that autoscraper can exactly match the items in your wanted_list 
wanted_list = ['A review']     # replace with item(s) of interest

# build the scraper
scraper = AutoScraper()
result = scraper.build(url, wanted_list)

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groups = scraper.get_result_similar(url, grouped=True)

Since groups is a dictionary, you can get the names of the rules by calling

groups.keys()

You can then key into the dictionary using a particular review.

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#0
from autoscraper import AutoScraper

#1
url = "https://www.yelp.com/biz/chun-yang-tea-flushing-new-york-flushing?osq=bubble%20tea&sort_by=date_desc"

#2
wanted_list = ['Tried their Brown Sugar milk tea and it was not bad compare to Tiger Sugar. I prefer this over Tiger Sugar due to the L size option and sweetness content. It was my to go bubble tea spot for the last two days straight. Will visit again!']

#3

XWilliamY

from bs4 import BeautifulSoup
url = "https://www.yelp.com/biz/chun-yang-tea-flushing-new-york-flushing?osq=bubble%20tea&sort_by=date_desc"
browser.get(url)

response = BeautifulSoup(browser.page_source, 'html.parser')
published = response.find_all('span', class_='lemon--span__373c0__3997G raw__373c0__3rKqk')

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from selenium import webdriver

def setup_browser(driver_path=None, headless=False):
    op = webdriver.ChromeOptions()
    if headless:
        op.add_argument('headless')

    if driver_path:
        return ebdriver.Chrome(driver_path, options=op)
    # this depends on where you install chromedriver

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# 0: returns the detected language
detected.lang

# 1: returns the confidence in the predicted language
detected.confidence

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# 0: returns the detected source language
translated.src

# 1: returns the language the text has been translated to
translated.dest

# 2: returns the original text
translated.origin

# 3: returns the translated text

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#0 import libraries
from googletrans import Translator
import multiprocessing
import workers

#1 create Translator object
translator = Translator()

#2 create multiprocessing pool
pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())

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from googletrans import Translator
translator = Translator()


def google_translate(text, dest='en'):
    return translator.translate(text, dest=dest)

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if isinstance(text, list):
    result = []
    for item in text:
        translated = self.translate(item, dest=dest, src=src, **kwargs)
        result.append(translated)
    return result