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Web scraping is a useful tool, but it could also rise to ethical and legal ambiguity. To begin with, a website can receive thousands of requests each second. Browsing at such amazing speeds is bound to attract attention. Such large amounts of requests are likely to clog up a website's servers and, in extreme situations, might be deemed a denial-of-service attack. Similarly, any website that requires a login may contain information that is not regarded as public as a result, and scraping such websites could put you in legal trouble. A programmer may be required to work with Google (Map) Reviews in a variety of situations. Anyone experienced with the Google My Business API knows that getting reviews using the API requires an account ID for each location (business). Scraping reviews can be quite useful in situations when a developer wants to work with evaluations from several sites (or does not have access to a Google business account). What is Web Scraping? The technique of obtaining

A guide to using Python to   Scrape Android App Reviews   and turn the data into a sentiment analysis database. Let's look at how to scrape reviews and ratings for Android apps to produce a dataset for sentiment analysis. You'll save the material to CSV files after converting the application and reviewing the data into Data Frames. Executing the code with Scripting with Pytorch (Google Colab) Installing necessary packages and setting up the imports You'll learn how to: Establish an objective and criteria for including your dataset. Look for real-world consumer comments on the internet. Use Pandas to convert and store the dataset into CSV files, which you can find on Google Play. The Dataset's Purpose Setup: import json import pandas as pd from tqdm import tqdm import seaborn as sns import matplotlib.pyplot as plt from pygments import highlight from pygments.lexers import JsonLexer from pygments.formatters import TerminalFormatter from google_play_scraper import Sort, re