Sentiment Community Detection Algorithm for Sina Weibo

doi:10.12068/j.issn.1005-3026.2021.01.004

Abstract

Abstract: Sentiment community detection in social networks would be very valuable in many areas， such as public health， public opinion monitoring and so on. A framework of sentiment community detection was established on Sina Weibo. Firstly， the sentiment expression features and lexicon of Weibo were combined， and the classification model SL-SE-NB (naive Bayes algorithm based semi-lexicon and semi-emoji) was proposed to predict the sentiment polarity of texts. And then， the UTK (user-topic-keywords) model based on LDA (latent Dirichlet allocation) was proposed to extract user topics. Based on LPA(label propagation algorithm) and adding topic concepts， SMB-LPA (label propagation algorithm based seeds and min-edge betweenness) was proposed to discovery sentiment community. Finally， the experimental results proved the effectiveness and efficiency of the proposed algorithms.

Key words: social network; sentiment community detection; sentiment analysis; topic model; LDA(latent Dirichlet allocation)

CLC Number:

TP391

HAN Dong-hong， ZHANG Hong-liang， ZHU Shuai-wei， QI Xiao-long. Sentiment Community Detection Algorithm for Sina Weibo[J]. Journal of Northeastern University(Natural Science), 2021, 42(1): 21-31.

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