Mining social media data based on Python web crawler technology --Taking Sina Weibo data analysis as an example

In the context of the big data era, the extensive penetration of the Internet and the rapid development of database technology have led to an explosive growth in the amount of data generated by human beings in their daily life, study and work. In the face of massive and complex data, how to extract valuable information from it has become an urgent problem. In this paper, we take the Sina Weibo platform as the research object, use web crawler technology to obtain data, and apply the Bayesian network model to analyze the sentiment of Weibo hot search data, aiming to provide a scientific basis for the control of public opinion orientation. The research framework integrates the technologies of big data crawling, natural language processing, and sentiment analysis, which not only provides a new perspective for public opinion monitoring but also a powerful tool for public opinion management. The study results show that in the era of big data, efficient processing and interpretation of large-scale information is of great significance for the effective management of public opinion.