Open AccessData And Content Analysis For Social Network Using LDA Text Model
Author(s) -
Linxiang Zhang
Publication year - 2019
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 - 10.1088/1742-6596/1213/2/022035
Subject(s) - latent dirichlet allocation , topic model , computer science , tacit knowledge , social network analysis , information retrieval , content analysis , natural language processing , social media , artificial intelligence , data science , world wide web , knowledge management , social science , sociology
WhatsApp is a popular cross-platform application for communicating and connecting people. In this paper, the author analyzes massive data from WhatsApp public groups and obtains so-called tacit knowledge and social complex knowledge. During the processing, the author uses LDA(Latent Dirichlet Allocation) model to analyze the content which helps us to be aware of many aspects from public groups such as social trend, sentiment analysis and other hidden information. In the end, author get the results about topic for each group and the occurrence of words for each topic.