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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 510281, 11 pages
Research Article

A Multilayer Naïve Bayes Model for Analyzing User’s Retweeting Sentiment Tendency

Mengmeng Wang,1,2 Wanli Zuo,1,2 and Ying Wang1,2,3

1College of Computer Science and Technology, Jilin University, Changchun 130012, China
2Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Ministry of Education, Changchun 130012, China
3College of Mathematics, Jilin University, Changchun 130012, China

Received 28 May 2015; Accepted 17 August 2015

Academic Editor: Stefan Haufe

Copyright © 2015 Mengmeng Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user’s retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user’s network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user’s retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.