Dynamic Topic-Based Sentiment Analysis of Large-Scale Online News

Many of today's online news websites and aggregator apps have enabled users to publish their opinions without respect to time and place. Existing works on topic-based sentiment analysis of product reviews cannot be applied to online news directly because of the following two reasons: 1 The dynamic nature of news streams require the topic and sentiment analysis model also to be dynamically updated. 2 The user interactions among news comments can easily lead to inaccurate topic and sentiment extraction. In this paper, we propose a novel probabilistic generative model DTSA to extract topics and the specified sentiments from news streams and analyze their evolution over time simultaneously. DTSA incorporates a multiple timescale model into a generative topic model. Additionally, we further consider the links among news comments to avoid the error caused by user interactions. Finally, we derive distributed online inference procedures to update the model with newly arrived data and show the effectiveness of our proposed model on real-world data sets.

[1]  Yan Zhao,et al.  Sentiment Analysis on News Comments Based on Supervised Learning Method , 2014, MUE 2014.

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

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

[4]  Ivan Titov,et al.  Modeling online reviews with multi-grain topic models , 2008, WWW.

[5]  Stefan M. Rüger,et al.  Weakly Supervised Joint Sentiment-Topic Detection from Text , 2012, IEEE Transactions on Knowledge and Data Engineering.

[6]  Chong Wang,et al.  Continuous Time Dynamic Topic Models , 2008, UAI.

[7]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[8]  Yasushi Sakurai,et al.  Online multiscale dynamic topic models , 2010, KDD.

[9]  Alice H. Oh,et al.  A Hierarchical Aspect-Sentiment Model for Online Reviews , 2013, AAAI.

[10]  Eugene Agichtein,et al.  TM-LDA: efficient online modeling of latent topic transitions in social media , 2012, KDD.

[11]  Jianwen Zhang,et al.  Sentiment Topic Model with Decomposed Prior , 2013, SDM.

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

[13]  Andrew McCallum,et al.  Topics over time: a non-Markov continuous-time model of topical trends , 2006, KDD '06.

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

[15]  Sabine Loudcher,et al.  A Joint Model for Topic-Sentiment Evolution over Time , 2014, 2014 IEEE International Conference on Data Mining.

[16]  Claire Cardie,et al.  Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon , 2014, WASSA@ACL.

[17]  Guolong Chen,et al.  Topic sentiment trend model: Modeling facets and sentiment dynamics , 2012, 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE).