-
Extract customer reviews from Euronics Italy using Python
Euronics is a leading electronics retailer in Italy, offering a wide variety of products ranging from home appliances to the latest gadgets. Scraping customer reviews from Euronics involves several steps, starting with analyzing the webpage structure. Reviews on Euronics are typically displayed in a dedicated section of the product page, containing customer names, ratings, and written feedback. These reviews might also include additional metadata, such as the date of the review or whether the customer purchased the item.
The script starts by identifying the HTML elements and classes that encapsulate the review information. This is done using browser developer tools to inspect the product page. Once the relevant elements are identified, the Python script fetches the page content using the requests library and parses it using BeautifulSoup. It targets specific tags to extract reviewer names, star ratings, and comments. Handling edge cases, such as products without reviews or dynamically loaded content, ensures the scraper functions reliably.
Another key consideration is pagination. Many Euronics product pages display only a limited number of reviews per page, so the script may need to navigate through multiple pages to collect all reviews. This can be done by identifying the “Next Page” button and iterating through the pages until no more reviews are available. Below is the Python implementation for scraping customer reviews from Euronics Italy:import requests from bs4 import BeautifulSoup # URL of the Euronics product page url = "https://www.euronics.it/product-page" # Headers to mimic a browser request headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36" } # Fetch the page content response = requests.get(url, headers=headers) if response.status_code == 200: soup = BeautifulSoup(response.content, "html.parser") # Extract reviews reviews = soup.find_all("div", class_="review-item") if reviews: for idx, review in enumerate(reviews, 1): reviewer_name = review.find("span", class_="reviewer-name") rating = review.find("div", class_="star-rating") comment = review.find("p", class_="review-comment") print(f"Review {idx}:") print("Reviewer:", reviewer_name.text.strip() if reviewer_name else "Anonymous") print("Rating:", rating.text.strip() if rating else "No rating") print("Comment:", comment.text.strip() if comment else "No comment") print("-" * 40) else: print("No reviews found.") else: print(f"Failed to fetch the page. Status code: {response.status_code}") -
4 Replies
-
The script could include additional functionality to handle dynamically loaded reviews. For example, integrating Selenium or Playwright would allow the scraper to interact with JavaScript-rendered content, ensuring all reviews are visible before extraction.
-
Improving error handling for edge cases, such as missing or incomplete reviews, would make the script more robust. Logging these cases for later analysis would help identify patterns and refine the scraper.
-
To make the script scalable, adding support for scraping reviews across multiple products would be useful. This can be achieved by iterating over a list of product URLs or dynamically collecting product links from category pages.
-
Saving the extracted reviews in a structured format like JSON or a relational database would improve data management. This would allow for efficient querying and analysis, particularly when handling large datasets or integrating with analytics tools.
Log in to reply.