Web Scraping

Web scraping is a technique to automatically access and extract large amounts of information from a website, which can save a huge amount of time and effort.

Important notes about web scraping:

Read through the website’s Terms and Conditions to understand how you can legally use the data. Most sites prohibit you from using the data for commercial purposes.
Make sure you are not downloading data at too rapid a rate because this may break the website. You may potentially be blocked from the site as well.

Techniques

Web scraping is the process of automatically mining data or collecting information from the World Wide Web. It is a field with active developments sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, semantic understanding, artificial intelligence and human-computer interactions. Current web scraping solutions range from the ad-hoc, requiring human effort, to fully automated systems that are able to convert entire web sites into structured information, with limitations.

Human copy-and-paste

The simplest form of web scraping is manually copying and pasting data from a web page into a text file or spreadsheet. Sometimes even the best web-scraping technology cannot replace a human’s manual examination and copy-and-paste, and sometimes this may be the only workable solution when the websites for scraping explicitly set up barriers to prevent machine automation.

Text pattern matching

A simple yet powerful approach to extract information from web pages can be based on the UNIX grep command or regular expression-matching facilities of programming languages (for instance Perl or Python).

HTTP programming

Static and dynamic web pages can be retrieved by posting HTTP requests to the remote web server using socket programming.

HTML parsing

Many websites have large collections of pages generated dynamically from an underlying structured source like a database. Data of the same category are typically encoded into similar pages by a common script or template. In data mining, a program that detects such templates in a particular information source, extracts its content and translates it into a relational form, is called a wrapper. Wrapper generation algorithms assume that input pages of a wrapper induction system conform to a common template and that they can be easily identified in terms of a URL common scheme. Moreover, some semi-structured data query languages, such as XQuery and the HTQL, can be used to parse HTML pages and to retrieve and transform page content.

DOM parsing

Further information: Document Object Model By embedding a full-fledged web browser, such as the Internet Explorer or the Mozilla browser control, programs can retrieve the dynamic content generated by client-side scripts. These browser controls also parse web pages into a DOM tree, based on which programs can retrieve parts of the pages. Languages such as Xpath can be used to parse the resulting DOM tree.

Vertical aggregation

There are several companies that have developed vertical specific harvesting platforms. These platforms create and monitor a multitude of “bots” for specific verticals with no “man in the loop” (no direct human involvement), and no work related to a specific target site. The preparation involves establishing the knowledge base for the entire vertical and then the platform creates the bots automatically. The platform’s robustness is measured by the quality of the information it retrieves (usually number of fields) and its scalability (how quick it can scale up to hundreds or thousands of sites). This scalability is mostly used to target the Long Tail of sites that common aggregators find complicated or too labor-intensive to harvest content from.

Semantic annotation recognizing

The pages being scraped may embrace metadata or semantic markups and annotations, which can be used to locate specific data snippets. If the annotations are embedded in the pages, as Microformat does, this technique can be viewed as a special case of DOM parsing. In another case, the annotations, organized into a semantic layer, are stored and managed separately from the web pages, so the scrapers can retrieve data schema and instructions from this layer before scraping the pages.

Computer vision web-page analysis

There are efforts using machine learning and computer vision that attempt to identify and extract information from web pages by interpreting pages visually as a human being might.

Web Scraping best practices to follow to scrape without getting blocked

Respect Robots.txt
Make the crawling slower, do not slam the server, treat websites nicely
Do not follow the same crawling pattern
Make requests through Proxies and rotate them as needed
Rotate User Agents and corresponding HTTP Request Headers between requests
Use a headless browser like Puppeteer, Selenium or Playwright
Beware of Honey Pot Traps
Check if Website is Changing Layouts
Avoid scraping data behind a login
Use Captcha Solving Services