Web Scraping

Web scraping is the automated extraction of data from websites. Instead of copying text by hand, a tool or script visits pages, pulls out the parts you care about (body copy, titles, prices, reviews, search results) and saves them as rows in a spreadsheet or table. In MLforSEO material it is usually step one of a project, because every machine learning task needs a dataset to work on.

How it works

A scraper requests a page, reads the HTML that comes back and uses selectors to find the elements you want. Selectors are usually CSS paths, XPath expressions or regular expressions. The extracted values are written to a file, one row per page or item. JavaScript-heavy sites may need a rendering crawler that runs the page in a headless browser first.

There are three common routes, from least to most technical:

• No-code browser extensions, such as Instant Data Scraper, which MLforSEO recommends for collecting reviews from Google Business Profile, Amazon or G2.
• Crawler custom extraction, such as Screaming Frog‘s custom extraction, where you copy a CSS selector or XPath from your browser’s inspect tool and pull that element from every crawled URL.
• APIs and code: Python libraries such as BeautifulSoup for HTML, or a SERP data provider like the DataForSEO API or SerpApi when you need search results at scale.

How it’s used in SEO and marketing

• Building a text corpus of your own or competitor pages for entity extraction, topic modelling or text classification.
• Collecting customer reviews for sentiment analysis and review mining.
• Pulling SERP results and SERP features for keyword research, intent classification and competitor analysis.
• Gathering existing titles and meta descriptions to compare against queries before rewriting them.
• Scraping internal links and anchor text as the input for internal link opportunity analysis.

Example

In MLforSEO’s Screaming Frog tutorial, you open a blog post, right-click the main content block, copy its selector, and add it as a custom extraction rule. After crawling the blog, every URL has a column containing just its body copy, with navigation and footers left out. That clean text then goes straight into an NLP API or a clustering notebook.

Limitations and things to watch

• Check the site’s terms and robots.txt before scraping anything you do not own, and keep request rates polite.
• Scraping is fragile: a design change can break selectors silently, so spot-check outputs.
• Boilerplate leaks in easily. Navigation and footer text can dominate an entity or topic analysis if you scrape whole pages.
• Personal data in reviews or comments falls under data protection law, so store only what you need.
• For search results, a SERP API is usually more reliable and cheaper over time than maintaining your own scraper.

Learn it on MLforSEO

• Web Scraping of Page Content in 3 Easy Steps with Screaming Frog, the no-code walkthrough.
• Semantic Analysis of Customer Reviews with Machine Learning shows review scraping with Instant Data Scraper.
• 10 Ways to Automate SERP Analysis with AI collects SERPs through DataForSEO.
• Semantic ML-enabled Keyword Research course, including the lab on SERP collection with DataForSEO.