An Analysis of College Students' Emotional Tendency for Internet Hot Searchs Based on Python

In the past before the advent of the Internet, if people wanted to know all kinds of real-time news hot spots, they just had to pass it on by word of mouth or read newspapers. However, with the continuous development of Internet technology, such as microblog, blog, Tiktok, Zhihu, Baidu Search, Guge Search and other platforms, they would constantly update real-time consultation. In order to help people analyze the emotional tendencies of college students on the Internet hot searchs, this paper proposes a method for analyzing the emotional tendencies of Internet hot searchs. Considering the strong artificial intelligence ecosystem of Python language, the whole program implementation process is implemented in Python language. Firstly, the network hot spot information data is crawled through the network crawler technology to obtain the latest network hot spot information; Then use the popular natural language processing methods (Python language and third-party packages and modules) to carry out keyword extraction, part of speech analysis and marking, and content emotion analysis of the probability and proportion of the three polarity (positive, neutral, negative). Through a large number of experiments and simulation tests, this method is simple and efficient, and can help to obtain and master the emotional tendencies of college students on hot issues on the Internet in real time.