A short text sentiment-topic model for product reviews

Abstract Topic and sentiment joint modelling has been successfully used in sentiment analysis for product reviews. However, the problem of text sparse is universal with the widespread smart devices and the shorter product reviews. In this paper, we propose a joint sentiment-topic model WSTM (Word-pair Sentiment-Topic Model) for the short text reviews, detecting sentiments and topics simultaneously from the text, especially considering the text sparse problem. Unlike other topic models modelling the generative process of each document, our directly models the generation of the word-pair set from the whole global corpus. In the generative process of WSTM, all of the words in a sentence have the same sentiment polarity, and two words in a word-pair have the same topic. We apply WSTM to two real-life Chinese product review datasets to verify its performance. In three experiments, compared with the existing approaches, the results demonstrate WSTM is quantitatively effective on both topic discovery and document level sentiment.

[1]  Sinno Jialin Pan,et al.  Short and Sparse Text Topic Modeling via Self-Aggregation , 2015, IJCAI.

[2]  Brian D. Davison,et al.  Empirical study of topic modeling in Twitter , 2010, SOMA '10.

[3]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[4]  Xiaohui Yan,et al.  A biterm topic model for short texts , 2013, WWW.

[5]  Michal Rosen-Zvi,et al.  Hidden Topic Markov Models , 2007, AISTATS.

[6]  Chun-hung Li,et al.  Semantic Dependent Word Pairs Generative Model for Fine-Grained Product Feature Mining , 2011, PAKDD.

[7]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[8]  Yue Lu,et al.  Unsupervised discovery of opposing opinion networks from forum discussions , 2012, CIKM '12.

[9]  Martin Ester,et al.  On the design of LDA models for aspect-based opinion mining , 2012, CIKM.

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

[11]  Hong Cheng,et al.  The dual-sparse topic model: mining focused topics and focused terms in short text , 2014, WWW.

[12]  Ying Su,et al.  Joint Naïve Bayes and LDA for Unsupervised Sentiment Analysis , 2013, PAKDD.

[13]  Wei Gao,et al.  A link-bridged topic model for cross-domain document classification , 2013, Inf. Process. Manag..

[14]  Harith Alani,et al.  Automatically Extracting Polarity-Bearing Topics for Cross-Domain Sentiment Classification , 2011, ACL.

[15]  Bo Pang,et al.  Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.

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

[17]  Scott Sanner,et al.  Improving LDA topic models for microblogs via tweet pooling and automatic labeling , 2013, SIGIR.

[18]  Qiaozhu Mei,et al.  One theme in all views: modeling consensus topics in multiple contexts , 2013, KDD.

[19]  Sheng Wang,et al.  SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis , 2014, AAAI.

[20]  Yue Lu,et al.  Rated aspect summarization of short comments , 2009, WWW '09.

[21]  Hao Wang,et al.  A Sentiment-aligned Topic Model for Product Aspect Rating Prediction , 2014, EMNLP.

[22]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[23]  Xiaoyan Zhu,et al.  Exploring weakly supervised latent sentiment explanations for aspect-level review analysis , 2013, CIKM.

[24]  Ivan Titov,et al.  A Joint Model of Text and Aspect Ratings for Sentiment Summarization , 2008, ACL.

[25]  Pengtao Xie,et al.  Integrating Document Clustering and Topic Modeling , 2013, UAI.

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

[27]  Tingting He,et al.  An empirical study of topic-sensitive probabilistic model for expert finding in question answer communities , 2014, Knowl. Based Syst..

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

[29]  Subhabrata Mukherjee,et al.  Joint Author Sentiment Topic Model , 2014, SDM.

[30]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[31]  Hiroya Takamura,et al.  Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees , 2005, PAKDD.

[32]  Kun Yang,et al.  Dynamic non-parametric joint sentiment topic mixture model , 2015, Knowl. Based Syst..

[33]  Hongfei Yan,et al.  Comparing Twitter and Traditional Media Using Topic Models , 2011, ECIR.

[34]  Min Wu,et al.  Sentiment Analysis Based on Light Reviews , 2016 .

[35]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[36]  Martin Ester,et al.  ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews , 2011, SIGIR.

[37]  Yong Zhang,et al.  Sentiment Analysis for Online Reviews Using an Author-Review-Object Model , 2011, AIRS.

[38]  Xiaoyan Zhu,et al.  Sentiment Analysis with Global Topics and Local Dependency , 2010, AAAI.

[39]  Ian Goodfellow,et al.  Generative adversarial networks , 2020, Commun. ACM.

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

[41]  Hongfei Yan,et al.  Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid , 2010, EMNLP.

[42]  Siddharth Patwardhan,et al.  Feature Subsumption for Opinion Analysis , 2006, EMNLP.

[43]  Yalou Huang,et al.  Hashtag Graph Based Topic Model for Tweet Mining , 2014, 2014 IEEE International Conference on Data Mining.

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

[45]  Susumu Horiguchi,et al.  Learning to classify short and sparse text & web with hidden topics from large-scale data collections , 2008, WWW.

[46]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[47]  Xianghua Fu,et al.  Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon , 2013, Knowl. Based Syst..

[48]  Wray L. Buntine,et al.  Topic Model : Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon , 2014 .

[49]  Yue Lu,et al.  Latent aspect rating analysis without aspect keyword supervision , 2011, KDD.

[50]  Martin Ester,et al.  The FLDA model for aspect-based opinion mining: addressing the cold start problem , 2013, WWW.

[51]  Zhen Lin,et al.  Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification , 2014, Knowl. Based Syst..

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