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  • How can I scrape product reviews from Bol.com using Python?

    Posted by Esfir Avinash on 12/21/2024 at 10:11 am

    Scraping product reviews from Bol.com using Python allows you to analyze customer feedback, review ratings, and product descriptions, which can be useful for market research and competitive analysis. Bol.com is a leading online retailer in the Netherlands, offering a wide range of products, from books to electronics. Scraping this data requires you to identify the HTML elements containing the reviews and ratings. Python’s HTTP and HTML parsing libraries can fetch and process this content efficiently. Pagination is another factor to consider since reviews are often spread across multiple pages. Automating navigation through pages ensures that all data is collected for a comprehensive dataset.
    The scraping process begins by inspecting the HTML structure of Bol.com. Tags and classes specific to reviews, such as customer names, ratings, and review content, must be located. By sending HTTP requests and parsing the HTML, you can retrieve this data programmatically. Introducing random delays between requests reduces detection risks, while saving the data in structured formats, like CSV or JSON, makes it easy to analyze. Below is an example script for scraping reviews from Bol.com.

    import requests
    from html.parser import HTMLParser
    class BolReviewsParser(HTMLParser):
        def __init__(self):
            super().__init__()
            self.in_review = False
            self.in_rating = False
            self.reviews = []
            self.current_review = {}
        def handle_starttag(self, tag, attrs):
            attrs = dict(attrs)
            if tag == "div" and "class" in attrs and "review-content" in attrs["class"]:
                self.in_review = True
            if tag == "span" and "class" in attrs and "rating" in attrs["class"]:
                self.in_rating = True
        def handle_endtag(self, tag):
            if self.in_review and tag == "div":
                self.in_review = False
            if self.in_rating and tag == "span":
                self.in_rating = False
        def handle_data(self, data):
            if self.in_review:
                self.current_review["review"] = data.strip()
            if self.in_rating:
                self.current_review["rating"] = data.strip()
                self.reviews.append(self.current_review)
                self.current_review = {}
    url = "https://www.bol.com/nl/nl/"
    response = requests.get(url)
    parser = BolReviewsParser()
    parser.feed(response.text)
    for review in parser.reviews:
        print(f"Review: {review['review']}, Rating: {review['rating']}")
    

    This script extracts reviews and ratings from Bol.com product pages. Pagination logic can be implemented to scrape reviews across multiple pages, ensuring complete data collection. Adding random delays between requests prevents detection and ensures a smoother scraping process.

    Thietmar Beulah replied 1 week, 2 days ago 2 Members · 1 Reply
  • 1 Reply
  • Thietmar Beulah

    Member
    01/01/2025 at 11:08 am

    One way to improve the scraper is by adding functionality to filter reviews based on keywords. For instance, focusing on reviews that mention specific product features can provide deeper insights into customer satisfaction. Another consideration is handling user-generated content that may include emojis or special characters, ensuring these are properly encoded. This feature enhances the scraper’s utility for more targeted analysis. Storing data in a relational database could further streamline data retrieval and analysis.

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