A novel method for recognizing emotions of weblog sentences

With plenty of online resources constantly increasing (like weblog, product reviews, news reviews, etc.), it is difficult to read them and obtain the useful information, especially emotion information. The emotion analysis on internet online information has received much attention from natural language processing field in recent years. In most existing works, single-label emotion analysis have been studied by many scientists, it often ignores the complexity of human feelings. This paper is dedicated to construct the multi-label emotion topic model for recognizing the complicated emotions of weblog sentences based on Chinese emotion corpus Ren-CECps. We employ latent topic variables and emotion variables to find complex emotions of the sentence. The results of experiments indicate that the model is reasonable and effective in recognizing the mixed emotions of weblog sentences.

[1]  Xin Kang,et al.  Employing hierarchical Bayesian networks in simple and complex emotion topic analysis , 2013, Comput. Speech Lang..

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

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

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

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

[6]  Fuji Ren,et al.  Estimation of word emotions based on part of speech and positional information , 2011, Comput. Hum. Behav..

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

[8]  Fuji Ren,et al.  Emotion Recognition of Weblog Sentences Based on an Ensemble Algorithm of Multi-label Classification and Word Emotions , 2012 .

[9]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[10]  Changqin Quan,et al.  A blog emotion corpus for emotional expression analysis in Chinese , 2010, Comput. Speech Lang..

[11]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

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

[13]  Lei Zhang,et al.  A Survey of Opinion Mining and Sentiment Analysis , 2012, Mining Text Data.

[14]  Fuji Ren,et al.  Affective Information Processing and Recognizing Human Emotion , 2006, MFCSIT.

[15]  Rada Mihalcea,et al.  Sentiment Analysis , 2014, Encyclopedia of Social Network Analysis and Mining.

[16]  Dipankar Das,et al.  Extracting emotion topics from blog sentences: use of voting from multi-engine supervised classifiers , 2010, SMUC '10.

[17]  Fuji Ren,et al.  From Cloud Computing to Language Engineering, Affective Computing and Advanced Intelligence ∗ , 2010 .

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

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

[20]  Bing Qin,et al.  Sentiment Analysis: Sentiment Analysis , 2010 .

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

[22]  Fu Wei,et al.  Unsupervised Topic and Sentiment Unification Model for Sentiment Analysis , 2013 .

[23]  Changqin Quan,et al.  Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing , 2012, Information Technology and Management.

[24]  Delip Rao,et al.  Semi-Supervised Polarity Lexicon Induction , 2009, EACL.

[25]  Plaban Kumar Bhowmick.  Reader Perspective Emotion Analysis in Text through Ensemble based Multi-Label Classification Framework , 2009, Comput. Inf. Sci..

[26]  Grigorios Tsoumakas,et al.  Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..

[27]  Carlo Strapparava,et al.  Learning to identify emotions in text , 2008, SAC '08.