Data is gold, and more and more companies are learning that. The internet is full of valuable information that can be used to get a competitive advantage in many fields. Companies use data to build new business strategies, predict trends, protect their brand, and even decide on product pricing.
The big question is, how to get this priceless data? The answer is web scraping. Some companies choose to build their own tools for extracting data from web pages, while others choose ready-to-use solutions for their web scraping projects.
If you’re just starting to familiarize yourself with automated data extraction, the best place to start is by learning about web scraping techniques. This information can help you get a better understanding of how automated web scraping tools work, and what scraping technique is the best for your case. To help you out, we reviewed eight web scraping techniques that are most relevant this year.
What is Web Scraping?
In simple terms, web scraping refers to automated data collection from various websites. The scraped data can include text, images, videos, or any other web content. The process can be performed by building an in-house web scraper or utilizing ready-to-use web scraping tools and solutions.
Web scraping has many synonyms that you may be familiar with. Web data extraction, data collection, data mining, and similar terms often refer to the same process of web scraping. You may even hear web crawling as a synonym for web scraping, although there are differences between these two processes.
Web Scraping Applications
Web scraping has many use cases. Companies and individuals scrape data from the web for various reasons. Some companies build their entire businesses on collected data. For example, flight fare aggregators scrape flight price data in real-time and provide it to users so they can conveniently compare ticket prices from different airlines.
This is just one example to give you a better understanding of web scraping applications. Companies in different industries utilize web scraping for various reasons, including e-commerce, marketing, academic research, etc. Here are the most popular use cases:
- Market Research — companies collect data about their competitors and use it for building a competitive business strategy.
- Lead Generation — web scraping can provide valuable information for generating potential leads. For example, this data can be found in various relevant forums.
- Brand Monitoring — businesses track their brand mentions to protect their brand and reputation. Real-time data allows companies to react to brand mentions quickly.
- Search Engine Optimization (SEO) — companies collect data to find the most popular queries among their potential customers and apply this data to build SEO content.
- Pricing Intelligence — scraping data from competitors’ marketplaces allows companies to implement dynamic pricing strategies. It helps generate more revenue and improve stock management.
- Machine Learning (ML) — automated web scraping helps train ML algorithms.
- Sentiment Analysis — gathering data from social media networks provides valuable information for sentiment analysis. This can help predict various future trends.
These are just some of the most popular use cases for scraped data. Companies use various techniques to collect data at scale, and we’ll review them in the following section.
Web Scraping Techniques
1. Manual Copy-Paste
While web scraping is usually an automated process, data can also be gathered manually. The manual copy-paste technique is done by humans rather than automated systems. This happens when someone copies data from a source and pastes it into a dedicated sheet.
While manual data collection is timely and often used for small-scale projects, it has a few pros. Copy-pasting data allows checking every data point and helps avoid errors or skip irrelevant information. The user is also very unlikely to get blocked by the target website.
Compared to automated scraping, the main downside of the manual copy-paste technique is the speed and scale of web scraping.
2. Matching Text Pattern
Text pattern matching-based web scraping is a rather simple technique, yet, it’s also powerful. This approach can be based on the Regular Expressions (RegEx) method that matches particular patterns using provided combinations. It can function as a filter for getting the desired output.
One of the main benefits of this scraping technique is the fact that RegEx is universal, meaning it works with all the programming languages.
3. HTTP Programming
This automated scraping technique functions by posting HTTP requests to remote web servers. The requests can be sent using socket programming. HTTP programming technique retrieves both static and dynamic web pages.
4. DOM Parsing
Document Object Model (DOM) defines the content and style structure of XML files. Embedding web browsers like Mozilla Firefox or Internet Explorer enables programs to extract dynamic content, which is generated by the client-side scripts, thus creating a DOM tree.
Web scrapers use DOM parsers in order to get a wholesome view of a website’s structure. Another use case for DOM parsers is for scrapers to get nodes with information. Programs then use various tools, such as XPath to extract data.
5. HTML Parsing
HTML Parsing is a quick and powerful web scraping method. It can be used to extract text or other resources, as well as for screen scraping. HTML parsing is done with JavaScript, and it targets nested or linear HTML pages.
6. Vertical Aggregation
Web scraping tools such as vertical aggregation platforms are created for obtaining data from certain verticals. These platforms manage many bots for specific verticals without direct human involvement and aren't related to a certain target website.
To prepare for vertical aggregation, it’s required to establish a knowledge base for a selected vertical. The platform then automatically creates bots based on the vertical’s logic.
How robust the platform is can be measured by the quality of retrieved information, such as the number of fields. Another important factor is the scalability of the platform. It can be defined by how quickly the tool can scale up in order to extract data from thousands of web pages.
7. Recognizing Semantic Annotation
Metadata, semantic markups, and annotations can be used to locate data snippets on the target sites. In the case of annotations being embedded in the pages, this web scraping technique resembles another one that we’ve already mentioned - DOM parsing.
If annotations are organized into a semantic layer and managed and stored separately from the target site, a web scraper can extract data schema and instructions from the semantic layer before scraping.
8. Web Page Analysis via Computer Vision
Computer vision is a rapidly developing web scraping technique. With the help of machine learning algorithms, computer vision can enable users to extract accurate and structured data from the web while preserving data quality and accuracy.
This web scraping tool is powerful and has a lot of potential. Computer vision can analyze images, read handwriting and text in images, as well as analyze video content in real-time. In the near future, this technique may become an irreplaceable tool for various web scraping projects.
Conclusion
Web scraping is a process of extracting data from the target websites. It can be done automatically or manually, for smaller web scraping projects. Companies use various automated web scraping tools to gather large amounts of data to fulfill their business needs. Web data can be used for market research, pricing intelligence, brand monitoring, lead generation, sentiment analysis, and many other business cases.
Companies choose different ways of data mining. They can build their own web scrapers or pick ready-to-use scraping tools. The most popular techniques are automated and depend on the scale of web scraping projects. Developers build DOM trees, use matching text pattern technique, or even employ computer vision to extract web data.
