Harika Bonthu — June 15, 2021
Data Mining Intermediate Libraries Programming Project Python

This article was published as a part of the Data Science Blogathon

Web Scraping beautifulsoup
Source

Intro:

According to the experts, 80 percent of all global data is unstructured. It could be photographs, documents, audio and video recordings, and web content. To make use of the information contained in it, we need to extract it and find patterns/draw useful insights. But how do we get that unstructured data into a structured format? This is where Web Scraping comes into the picture.

 

What is Web Scraping:

In simple terms, Web scraping, web harvesting, or web data extraction is an automated process of collecting large data(unstructured) from websites. The user can extract all the data on particular sites or the specific data as per the requirement. The data collected can be stored in a structured format for further analysis.

 

Uses of Web Scraping:

In today’s world, web scraping has gained a lot of attention and has a wide range of uses. A few of them are listed below:

  1. Social Media Sentiment Analysis
  2. Lead Generation in Marketing Domain
  3. Market Analysis, Online Price Comparison in eCommerce Domain
  4. Collect train and test data in Machine Learning Applications

Steps involved in web scraping:

  1. Find the URL of the webpage that you want to scrape
  2. Select the particular elements by inspecting
  3. Write the code to get the content of the selected elements
  4. Store the data in the required format

It’s that simple guys..!!

The popular libraries/tools used for web scraping are:

  • Selenium – a framework for testing web applications
  • BeautifulSoup – Python library for getting data out of HTML, XML, and other markup languages
  • Pandas – Python library for data manipulation and analysis

In this article, we will be building our own dataset by extracting Domino’s Pizza reviews from the website consumeraffairs.com/food.

We will be using requests and BeautifulSoup for scraping and parsing the data.

Step 1: Find the URL of the webpage that you want to scrape

Open the URL “consumeraffairs.com/food” and search for Domino’s Pizza in the search bar and hit Enter.

 

Find the URL beautifulsoup library

Below is how our reviews page looks like.

 

review page looks beautifulsoup library

Step 1.1: Defining the Base URL, Query parameters

Base URL is the consistent part of your web address and represents the path to the website’s search functionality.

base_url = "https://www.consumeraffairs.com/food/dominos.html?page="

Query parameters represent additional values that can be declared on the page.

query_parameter = "?page="+str(i) # i represents the page number

 

Defining the Base URL beautifulsoup library
URL = Base URL + Query Parameter (Image by Author)

Step 2: Select the particular elements by inspecting

Below is an image of a sample review. Each review has many elements: the rating given by the user, username, review date, and the review text along with some information about how many people liked it.

Step 2: Select the particular elements by inspecting

Our interest is to extract only the review text. For that, we need to Inspect the page and obtain the HTML tags, attribute names of the target element.

To inspect a web page, right-click on the page, select Inspect, or use the keyboard shortcut Ctrl+Shift+I.

In our case, the review text is stored in the HTML <p> tag of the div with the class name “rvw-bd

text is stored in the HTML
Inspecting the target elements

With this, we got familiar with the webpage. Let’s quickly jump into the scraping.

 

Step 3: Write the code to get the content of the selected elements

Begin with installing the necessary modules/packages

pip install pandas requests BeautifulSoup4

Import necessary libraries

import pandas as pd
import requests
from bs4 import BeautifulSoup as bs

pandas – to create a dataframe
requests – to send HTTP requests and access the HTML content from the target webpage
BeautifulSoup – is a Python Library for parsing structured HTML data

Create an empty list to store all the scraped reviews

all_pages_reviews = []

define a scraper function

def scraper():

Inside the scraper function, write a for loop to loop through the number of pages you would like to scrape. I would like to scrape the reviews of five pages.

for i in range(1,6):

Creating an empty list to store the reviews of each page(from 1 to 5)

pagewise_reviews = []

Construct the URL

url = base_url + query_parameter

Send HTTP request to the URL using requests and store the response

response = requests.get(url)

Create a soup object and parse the HTML page

soup = bs(response.content, 'html.parser')

Find all the div elements of class name “rvw-bd” and store them in a variable

rev_div = soup.findAll("div",attrs={"class","rvw-bd"})

Loop through all the rev_div and append the review text to the pagewise_reviews list

for j in range(len(rev_div)):
			# finding all the p tags to fetch only the review text
			pagewise_reviews.append(rev_div[j].find("p").text)

Append all pagewise review to a single list “all_pages_reviews”

for k in range(len(pagewise_reviews)):
   all_pages_reviews.append(pagewise_reviews[k])

At the end of the function, return the final list of reviews

return all_pages_reviews
Call the function scraper() and store the output to a variable 'reviews'
# Driver code
reviews = scraper()

Step 4: Store the data in the required format

4.1 storing to a pandas dataframe

i = range(1, len(reviews)+1)
reviews_df = pd.DataFrame({'review':reviews}, index=i)
Now let us take a glance of our dataset
print(reviews_df)
storing to a pandas dataframe

4.2 Writing the content of the data frame to a text file

reviews_df.to_csv('reviews.txt', sep='t')

With this, we are done with extracting the reviews and storing them in a text file. Mmm, it’s pretty simple, isn’t it?

Complete Python Code:

# !pip install pandas requests BeautifulSoup4 
import pandas as pd
import requests
from bs4 import BeautifulSoup as bs
base_url = "https://www.consumeraffairs.com/food/dominos.html"
all_pages_reviews =[]
  1. def scraper():
        for i in range(1,6): # fetching reviews from five pages
            pagewise_reviews = [] 
            query_parameter = "?page="+str(i)
    	url = base_url + query_parameter
    	response = requests.get(url)
    	soup = bs(response.content, 'html.parser') 
    	rev_div = soup.findAll("div",attrs={"class","rvw-bd"}) 
    
        for j in range(len(rev_div)):
        # finding all the p tags to fetch only the review text
            pagewise_reviews.append(rev_div[j].find("p").text)
    
        for k in range(len(pagewise_reviews)):
            all_pages_reviews.append(pagewise_reviews[k]) 
            return all_pages_reviews
    
    # Driver code
    reviews = scraper()
    i = range(1, len(reviews)+1)
    reviews_df = pd.DataFrame({'review':reviews}, index=i)
    reviews_df.to_csv('reviews.txt', sep='t')

End Notes:

By the end of this article, we have learned the step-by-step process of extracting content from any given web page and storing them in a text file.

  • inspect the target element using the browser’s developer tools
  • use requests to download the HTML content
  • parse the HTML content using BeautifulSoup to extract required data

We can further develop this example by scraping usernames, review text. Perform vectorization on the cleaned review text, and group the users according to the reviews written. We can use Word2Vec or CounterVectorizer to convert text to vectors and apply any of the Machine Learning clustering algorithms.

 

References:

BeautifulSoup library: Documentation, Video Tutorial

DataFrame to CSV

GitHub Repo Link to download the source code

I hope this blog helps understand web scraping in Python using the BeautifulSoup library. Happy learning !! 😊

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