Game theory based emotional evolution analysis for chinese online reviews

Sentiment analysis has become one of the mainstream researches in social network analysis. Its impact can be seen in many practical applications, ranging from public opinion analysis to marketing of public praise and information prediction. However, most of the existing research has been performed in the sentiment classification for subjective text, the emotional evolution analysis for complex interactive text (e.g., online reviews) has not yet been thoroughly targeted by the research community. This paper is concerned on short-text Chinese online reviews collected from Tianya forum. First, an efficient affective computing framework is proposed to capture the underlying emotions of Chinese online reviews. It can accurately calculate the semantic orientation of the entire review, without requiring expensive manual labeling of seed words. As users' attitudes might influence with each other, predicting their future emotional behaviors that only relying on the emotional values of historical reviews is very one-sided. Therefore, we propose a game theory based emotional evolution prediction algorithm combining the affective computing, in which the mixed nash equilibrium strategies are calculated as the future emotional behavior of interactive users. Then, experimental results on the large-scaled review dataset are provided to demonstrate the effectiveness and accurateness of our approaches. Finally, by applying the research results on the pairwise happiness-popularity coordination evaluation, we have revealed some interesting phenomenon on the "World View" board in Tianya forum.

[1]  Xiaohui Yu,et al.  Riding the tide of sentiment change: sentiment analysis with evolving online reviews , 2013, World Wide Web.

[2]  Milad Shokouhi,et al.  Behavioral dynamics on the web: Learning, modeling, and prediction , 2013, TOIS.

[3]  Ellen Riloff,et al.  Learning Extraction Patterns for Subjective Expressions , 2003, EMNLP.

[4]  J. Michael Spector,et al.  Handbook of Research on Educational Communications and Technology, 3rd Edition , 2012 .

[5]  Michael L. Littman,et al.  Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.

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

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

[8]  Rosalind W. Picard.  Affective Computing , 1997 .

[9]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[10]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[11]  Zhan Bu,et al.  A sock puppet detection algorithm on virtual spaces , 2013, Knowl. Based Syst..

[12]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[13]  Abhinav Kumar,et al.  Spotting opinion spammers using behavioral footprints , 2013, KDD.

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

[15]  Ellen Riloff,et al.  Learning subjective nouns using extraction pattern bootstrapping , 2003, CoNLL.

[16]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[17]  Andrea Esuli,et al.  Determining the semantic orientation of terms through gloss classification , 2005, CIKM '05.

[18]  Vadlamani Ravi,et al.  A survey on opinion mining and sentiment analysis: Tasks, approaches and applications , 2015, Knowl. Based Syst..

[19]  Baowen Li,et al.  Cumulants of heat transfer across nonlinear quantum systems , 2012, 1210.2798.

[20]  Giuliano Armano,et al.  Emergence of acronyms in a community of language users , 2013 .

[21]  Jure Leskovec,et al.  Steering user behavior with badges , 2013, WWW.

[22]  Evgeniy Gabrilovich,et al.  A word at a time: computing word relatedness using temporal semantic analysis , 2011, WWW.

[23]  Giuliano Armano,et al.  Perception of similarity: a model for social network dynamics , 2013 .

[24]  Michael Chau,et al.  A Hybrid System for Online Detection of Emotional Distress , 2012, PAISI.

[25]  Marco Alberto Javarone.  Competitive dynamics of lexical innovations in multi-layer networks , 2013, ArXiv.

[26]  J. Kamps,et al.  Words with attitude , 2002 .

[27]  Marco Alberto Javarone.  The Role of the Shannon Entropy in the Identification of Acronyms , 2014, CompleNet.

[28]  Erik Cambria,et al.  Sentic patterns: Dependency-based rules for concept-level sentiment analysis , 2014, Knowl. Based Syst..

[29]  Vasileios Hatzivassiloglou,et al.  Predicting the Semantic Orientation of Adjectives , 1997, ACL.

[30]  Stefan Rank,et al.  Modelling Emotional Trajectories of Individuals in an Online Chat , 2012, MATES.

[31]  Razvan C. Bunescu,et al.  Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques , 2003, Third IEEE International Conference on Data Mining.

[32]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[33]  Xiaohui Yu,et al.  ARSA: a sentiment-aware model for predicting sales performance using blogs , 2007, SIGIR.

[34]  Edoardo M. Airoldi,et al.  Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts , 2004, WebKDD.

[35]  Andrea Tagarelli,et al.  Lurking in social networks: topology-based analysis and ranking methods , 2014, Social Network Analysis and Mining.

[36]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[37]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[38]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[39]  Janyce Wiebe,et al.  Learning Subjective Adjectives from Corpora , 2000, AAAI/IAAI.

[40]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[41]  Regina Barzilay,et al.  Multiple Aspect Ranking Using the Good Grief Algorithm , 2007, NAACL.

[42]  J. Nash.  THE BARGAINING PROBLEM , 1950, Classics in Game Theory.

[43]  Songbo Tan,et al.  A survey on sentiment detection of reviews , 2009, Expert Syst. Appl..

[44]  Rada Mihalcea,et al.  Learning Multilingual Subjective Language via Cross-Lingual Projections , 2007, ACL.

[45]  Satoshi Morinaga,et al.  Mining product reputations on the Web , 2002, KDD.

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

[47]  Ellen Riloff,et al.  Creating Subjective and Objective Sentence Classifiers from Unannotated Texts , 2005, CICLing.

[48]  Aoying Zhou,et al.  SentiView: Sentiment Analysis and Visualization for Internet Popular Topics , 2013, IEEE Transactions on Human-Machine Systems.

[49]  Andrea Tagarelli,et al.  Time-aware analysis and ranking of lurkers in social networks , 2015, Social Network Analysis and Mining.