When the number of pages and the complexity of websites to be scraped increase, each of these steps face with unique challenges.
Artificial intelligence methods help web scraping tools to overcome the unique challenges of each step. In this article, we will introduce you the top 3 ways that AI enable web scraping to overcome technical challenges which may be helpful as your web scraping needs scale up and get more complicated.
Collect Only the URLs You Need
Web scraping starts with targeting websites, such as “top 100 search results for this keyword” or “these 3 ecommerce websites for this product type”. This may sound easy on the surface, but the next step is to find exact URLs that match these targets, which is a challenge for web scraping. A web scraper needs to find the source URL and generate the target URLs for the required pages. While generating thousands of URLs, broken links and websites that have unrelated content cause the algorithm to waste time and data storage for scraping content that has no business value to the user.
AI helps web scraping to find and list URLs in two ways:
- Classification algorithms: Algorithms that are trained on big web scraping data sets are able to identify and classify URLs that are inactive. This helps web scraping algorithms to minimize the scraping effort to only a subset that are potentially helpful.
- Natural language processing algorithms: A new research suggests improvement to web scraping algorithms to scan the scraped data with natural language processing techniques in order to identify the relevancy of the content. This way, data that is below the relevancy threshold would not be saved at all, which optimizes the data storage and processing effort.
Find the right proxy for each website
Websites may try to block web scrapers in order not to receive excessive amount of traffic and interrupt their services. They do it by identifying the source and the behavior of the scraper through “browser fingerprinting” such as checking whether the same IP address is trying to scrape the website multiple times, device and operating system type of the scraper and how fast the requests are sent.
A research shows that once identified, these fingerprints can be tracked by the websites up to 54 days on average. This brings the need for web scrapers to anonymize themselves through a new origin in each web scraping request and change their behaviors similar to a human users while scraping a website.
A common solution for this challenge is called dynamic proxies, which means the web scraper to change their IP address dynamically in each web scraping request. However, there are other parameters that still helps websites to identify automated web scrapers.
AI solutions support dynamic proxy technology by optimizing the other parameters. Since each web scraping attempt generates a fingerprint on the scraper end, web scrapers can use this as a training data to make sure the new parameters they use are significantly different from the previous fingerprints they generated.
To select the most suitable proxy service for your business’s needs and requirements, check out:
Minimize the time spent on data parsing
An essential part of generating value from web scraping is data parsing and cleaning the data so that it can be analyzed for business insights. Scraped data from each website contains source code that can be in different programming languages, and also text data itself which is a classification and text processing task on its own.
When thousands of different web pages are scraped, especially different websites, but even the different pages of the same website can contain different structures which needs its own data parsing code. This process also needs maintenance because websites often change their structure which requires the data parsing algorithm to be updated.
AI methods can create adaptive parsing models that learn from experience. By using parsed data as a training set, parsing models can learn how to classify different parts of the scraped data and eliminate unnecessary parts efficiently.
Despite having different website structures, some of these elements identified may also be common across similar websites. For example, since many ecommerce websites have similar layouts to display the product image and details such as price, a data parsing algorithm may identify approximate location of a product’s image and details and uses this as a proxy to identify where to look for the required data in a different dataset
One way to use artificial intelligence effectively for web scraping solutions is to have multiple clients and a big volume of web scraping, which creates big amounts of data to train algorithms on.
Bright Data brings automation in place in their web scraping process to minimize the time and effort you would spend on collecting web data. By using the scale of their web collection, they also offer readily available web datasets which may already provide the insights you are looking for your business.
For more on web scraping:
To explore web scraping use cases for different industries, its benefits and challenges read our articles:
- Top 7 Web Scraping Best Practices You Must Be Aware of
- Watch-outs for Legal and Ethical Web Scraping
- Web Scraping Tools: Data-driven Benchmarking
For guidance to choose the right tool, check out data-driven list of web scrapers, and reach out to us:
This article was drafted by former AIMultiple industry analyst Bengüsu Özcan.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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