Open AccessLDA-based Topic Mining of Microblog Comments
Author(s) -
Xu Liu,
Ying Gao,
Ziping Cao,
Guang Sun
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012118
Subject(s) - microblogging , latent dirichlet allocation , social media , web crawler , computer science , crawling , construct (python library) , world wide web , theme (computing) , event (particle physics) , topic model , data science , information retrieval , medicine , physics , anatomy , quantum mechanics , programming language
Microblog comments express the public’s views and attitudes on Microblog hot topics and hot events. Therefore, by excavating the theme of Microblog comments, we can help people understand the trend of public opinion and the attitude of the public towards the event. In view of Microblog comment topic mining, the problem we need to solve is how to use simple methods to quickly and efficiently obtain Microblog comment content, and how to use what methods to mine the theme of Microblog hot events to a great extent. First of all, we construct dynamic links through static web pages, write Microblog comment web crawler program in Python language, and solve the problem of crawling Microblog dynamically loading web page comments. Secondly, we use the Latent Dirichlet Allocation model, which is the implicit Dirichlet distribution, to mine the text.