The key advantage of extracting data from websites is the ability to gather large amounts of information quickly and efficiently. This can be particularly useful for blogers, researchers or businesses needing to collect data for analysis or decision-making purposes.
However, the process is not without its problems. Firstly, websites may use anti-scraping mechanisms such as CAPTCHAs or IP blocking systems that impede data extraction efforts. Additionally, legal issues may arise if the terms of service of a website explicitly prohibit scraping activities. The quality and reliability of the extracted data might also be compromised due to inconsistencies in web page structures or changes made frequently by website owners.
That’s why it’s important to approach the process of extracting content from websites responsibly and apply well-designed web scraping and natural language processing tools, such as the Python programming language and its libraries boilerpipe, nltk, pymorphy, httplib, BeautifulSoup.
In today’s post, I will introduce you to a way to extract content from one of the largest blogging platforms in Russia – dzen.ru. This website is known for its ability to personalize news and articles based on users’ interests. By utilizing sophisticated algorithms, Dzen.ru curates content from various sources including news outlets, blogs, and social media platforms to provide an informative yet personalized user experience. Furthermore, the platform analyzes each user’s reading patterns and preferences to continuously improve article recommendations.
Due to this approach dzen.ru is attractive to millions of users and thousands of content authors, with dozens of thousands of articles published on the site every day. It is no wonder that many programmers and data scientists have a willingness to parse content from such a resource.
