How Artificial Intelligence Is Used in Web Scraping

How Artificial Intelligence Is Used in Web Scraping

Research shows that 93% of businesses believe accurate data helps them make critical decisions. Decisions that impact their clients positively, thus giving the business an edge over their competition. Therefore, it doesn’t matter whether you are web scraping for sentiment analysis or searching for the right keywords to optimize your content, you need to invest in the right tools that give you accurate and reliable results. How does artificial intelligence apply in web scraping?

A Quick Overview of AI

Artificial intelligence refers to the applications or computer systems that can learn and adapt, just like humans, without explicit instructions. In web scraping, the systems use machine learning algorithms to identify patterns in data and apply statistical methods to present results in useful formats.

Relevance of AI to Web Scraping

In simple terms, web scraping is extracting data from the web. The more accurate the data you have is, the more informed your decisions are. For instance, web scraping for analysis requires you to use large volumes of Google data from social media post previews, news headlines, and customer reviews. Going through the high volumes of data with conventional tools is hectic and prone to errors. This is where Google scraping tools with AI capabilities come in handy. They help you analyze this data efficiently and categorize customer opinion as either positive, negative, or neutral.

Ways You Can Use AI for Web Scraping

As mentioned above, web scraping gets complex, especially when handling large data sets. Here are ways you can use AI to make web scraping less painful.

1. Discovering and Building Crawling Links

Scraping data from the web starts with discovering and collecting URLs for website pages. These URLs serve as the crawling links used by web scraping tools to find the target data. However, not all links provide useful data. Some may take you off track. Luckily, AI-powered web scrapers learn to identify crawling links and build a library of relevant URLs. This way, it is possible to scale up your web scraping and access hundreds of web pages quickly.

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2. Automating Data Extraction and Parsing

Once you retrieve data from the internet using traditional web scrapers, you will need to sort and turn it into a meaningful format. This step is a major headache because many website formats keep on changing.

However, with AI, this won’t be a problem anymore. AI algorithms employ adaptive parsing techniques to analyze and identify data syntactically. You will not need to develop a parser and spend valuable time extracting data. The AI-powered scraper does all the work for you, allowing you to focus on what matters – data analysis.

3. Avoiding Anti Scraping Software 

Successful scraping of search engines for data comes with added advantages to businesses. Unfortunately, search engine owners have devised ways to deter data scraping. Additionally, some websites deploy anti-scraping bots on their sites as a security measure, and for good reasons. They do not know what the data scraped from their sites will be used for.

Not so long ago, web scrapers could avoid detection by changing IP addresses and user profiles. But this approach no longer works. Search engines use fingerprinting and behavioral detection to bolster their anti-scraping technologies. These improvements have resulted in low success rates for the average web scraping tools. Fingerprinting detection methods have proven to be successful against web scrapers.

A recent study on “Tracking Browser Fingerprint Evolutions” showed that you could only track 26% of browsers for more than 100 days. In most cases, you do it for 54 days. The solution to this problem lies with AI technology. Enhancing web scraping tools with artificial intelligence improves their success against anti-scraping bots.

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4. Enhance Proxy Performance and Management

Web scrapers use proxies to increase their success rates. Using tactics such as proxy rotation allows web scraping tools to avoid IP bans, access region-specific data, and increase the volume of accessible websites. Websites use IP detection to determine which users are legitimate. For instance, if a data center IP accesses a website repeatedly and retrieves data that is not publicly available, it is likely to be banned. With multiple mobile and residential IPs, web scrapers can access websites and avoid detection. However, this technique is not foolproof. You will still struggle with IP bans and constant maintenance.

Introducing AI-powered proxies to web scraping tools gives them unmatched stealth. AI avoids proxy blocking by making traffic from web scraping tools appear as if it is 100% from organic users. AI auto-retry capabilities allow you to get data that is difficult to access. Moreover, AI can beat regional access restrictions by selecting the relevant IPs from its proxy pools with millions of IP addresses.

Conclusion

Web scraping for sentiment analysis allows companies to monitor market perception about their brands and products. This way, they bolster their product design and marketing strategies to beat their competition. However, web scraping comes with a whole host of problems ranging from inaccurate data identification to deterrence by anti-scraping software. However, integrating AI in web scraping tools enhances their capabilities. Not only do users have higher success rates, but have access to large volumes of accurate data.

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