Monday, 13 January 2025

9. Advanced Use Cases in Web Scraping

Lecture Notes: Advanced Use Cases in Web Scraping

1. Introduction to Advanced Use Cases in Web Scraping

Web scraping is not limited to simple data extraction. Advanced use cases allow organizations and developers to extract, analyze, and utilize large-scale data for specific applications. This lecture covers key use cases, including scraping e-commerce data, gathering social media insights, aggregating news, and building custom web crawlers for large-scale data collection.


2. Scraping E-Commerce Sites for Product Data

Objective

Extract product information, including names, prices, ratings, and reviews, from e-commerce platforms for price comparison, market research, or inventory monitoring.

Challenges

  1. Dynamic content (JavaScript rendering).
  2. Anti-scraping mechanisms (CAPTCHA, rate limiting).
  3. Data volume and variability.

Solution

Use a combination of Selenium (for dynamic content) and BeautifulSoup (for parsing HTML).

Code Example

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from bs4 import BeautifulSoup
import pandas as pd

# Configure Selenium WebDriver
service = Service('path_to_chromedriver')
driver = webdriver.Chrome(service=service)

# Open e-commerce site
ecommerce_url = "https://example-ecommerce.com"
driver.get(ecommerce_url)

# Parse the page source with BeautifulSoup
soup = BeautifulSoup(driver.page_source, 'html.parser')

# Extract product data
products = []
product_elements = soup.find_all('div', class_='product-item')
for item in product_elements:
    name = item.find('h2', class_='product-name').text.strip()
    price = item.find('span', class_='price').text.strip()
    rating = item.find('span', class_='rating').text.strip()
    products.append({"Name": name, "Price": price, "Rating": rating})

# Save to CSV
driver.quit()
products_df = pd.DataFrame(products)
products_df.to_csv('ecommerce_products.csv', index=False)
print("Product data saved to ecommerce_products.csv")

3. Gathering Social Media Data (Twitter, Instagram, LinkedIn)

Objective

Collect user posts, hashtags, follower counts, and other relevant data for social media analytics.

Challenges

  1. API rate limits and authentication requirements.
  2. Strict platform policies against scraping.
  3. Dynamic and frequently changing DOM structures.

Solution

Use official APIs where available or headless browsers for scraping.

Code Example: Twitter API

import tweepy

# Twitter API credentials
api_key = 'your_api_key'
api_secret_key = 'your_api_secret_key'
access_token = 'your_access_token'
access_token_secret = 'your_access_token_secret'

# Authenticate
auth = tweepy.OAuthHandler(api_key, api_secret_key)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)

# Search tweets
query = "#WebScraping"
tweets = api.search_tweets(q=query, lang='en', count=100)

# Print tweets
for tweet in tweets:
    print(f"User: {tweet.user.screen_name}, Tweet: {tweet.text}")

4. News Aggregation and Sentiment Analysis

Objective

Scrape news articles, extract key content, and analyze sentiment for trend detection or reporting.

Challenges

  1. Handling different website structures.
  2. Extracting meaningful content from HTML noise.
  3. Large-scale text analysis.

Solution

Combine web scraping with Natural Language Processing (NLP) libraries.

Code Example

from bs4 import BeautifulSoup
import requests
from textblob import TextBlob

# Target news website
news_url = "https://example-news-site.com"
response = requests.get(news_url)
response.raise_for_status()

# Parse with BeautifulSoup
soup = BeautifulSoup(response.text, 'html.parser')
articles = []
article_elements = soup.find_all('div', class_='news-article')

for article in article_elements:
    title = article.find('h2').text.strip()
    summary = article.find('p', class_='summary').text.strip()
    sentiment = TextBlob(summary).sentiment.polarity
    articles.append({"Title": title, "Summary": summary, "Sentiment": sentiment})

# Print results
for article in articles:
    print(f"Title: {article['Title']}, Sentiment: {article['Sentiment']}")

5. Building Custom Web Crawlers for Large-Scale Data Collection

Objective

Crawl websites systematically to collect data across multiple pages and domains.

Challenges

  1. Managing crawling depth and breadth.
  2. Handling duplicate data and avoiding traps.
  3. Efficient storage of large datasets.

Solution

Use Scrapy for building efficient and scalable web crawlers.

Code Example

import scrapy

class QuotesSpider(scrapy.Spider):
    name = "quotes"
    start_urls = ["http://quotes.toscrape.com"]

    def parse(self, response):
        for quote in response.css('div.quote'):
            yield {
                'text': quote.css('span.text::text').get(),
                'author': quote.css('small.author::text').get(),
                'tags': quote.css('div.tags a.tag::text').getall(),
            }

        # Follow pagination links
        next_page = response.css('li.next a::attr(href)').get()
        if next_page is not None:
            yield response.follow(next_page, self.parse)

To run the spider:

scrapy runspider quotes_spider.py -o quotes.json

6. Conclusion

Advanced web scraping use cases open up opportunities for data-driven decision-making in e-commerce, social media analytics, news aggregation, and more. By leveraging tools like Selenium, BeautifulSoup, APIs, and Scrapy, developers can tackle complex scenarios efficiently.


Assignments and Quiz

Assignment: Create a custom crawler using Scrapy to collect data from a website of your choice. Save the data in JSON format.

Quiz Questions:

  1. What are the key challenges in scraping social media platforms?
  2. How does sentiment analysis help in news aggregation?
  3. What is the advantage of using Scrapy for large-scale web crawling?
  4. Which library is commonly used for sentiment analysis in Python?
  5. Name one way to handle dynamic content on e-commerce sites.

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