Topic sentiment trend model: Modeling facets and sentiment dynamics

Mining subtopics and analyzing their sentiment dynamics on weblogs have many applications in multiple domains. Current work pays little attention to the combination of topics and their sentiment evolution simultaneously. In this paper, we study the problem of topic detection and sentiment-topic temporal evolution in weblogs, and propose a novel probabilistic model called topic sentiment trend model (TSTM). With the model, we can integrate the topic with sentiment, and analyze the temporal trend of the sentiment-topic. Experiments on two Chinese weblog datasets show that our approach is effective in modeling the topic facets and extracting their sentiment dynamics.

[1]  Lucy T. Nowell,et al.  ThemeRiver: visualizing theme changes over time , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[2]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[3]  Yulan He,et al.  Joint sentiment/topic model for sentiment analysis , 2009, CIKM.

[4]  Noriaki Kawamae,et al.  Trend analysis model: trend consists of temporal words, topics, and timestamps , 2011, WSDM '11.

[5]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[6]  John D. Lafferty,et al.  Dynamic topic models , 2006, ICML.

[7]  Alice H. Oh,et al.  Aspect and sentiment unification model for online review analysis , 2011, WSDM '11.

[8]  Thomas L. Griffiths,et al.  Probabilistic Topic Models , 2007 .

[9]  Xu Ling,et al.  Topic sentiment mixture: modeling facets and opinions in weblogs , 2007, WWW '07.

[10]  ChengXiang Zhai,et al.  Discovering evolutionary theme patterns from text: an exploration of temporal text mining , 2005, KDD '05.

[11]  Min Zhang,et al.  A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval , 2008, SIGIR '08.

[12]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[13]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[14]  Philip S. Yu,et al.  A holistic lexicon-based approach to opinion mining , 2008, WSDM '08.

[15]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Chao Liu,et al.  A probabilistic approach to spatiotemporal theme pattern mining on weblogs , 2006, WWW '06.

[17]  Yueshen Xu,et al.  Topic Model , 2014, Encyclopedia of Social Network Analysis and Mining.

[18]  Hiroshi Nakagawa,et al.  Understanding Sentiment of People from News Articles: Temporal Sentiment Analysis of Social Events , 2007, ICWSM.

[19]  Yulan He,et al.  A Comparative Study of Bayesian Models for Unsupervised Sentiment Detection , 2010, CoNLL.

[20]  Jian Pei,et al.  Detecting topic evolution in scientific literature: how can citations help? , 2009, CIKM.

[21]  Lucy T. Nowell,et al.  ThemeRiver: Visualizing Thematic Changes in Large Document Collections , 2002, IEEE Trans. Vis. Comput. Graph..