An approach to feature selection for sentiment analysis

Sentiment analysis deals with methods for automatic analysis of the subjective aspects of the text. In this contribution we first present an overview of main approaches currently used in sentiment analysis. We further focus on feature selection methods for sentiment analysis and propose a new approach to feature selection. Our approach has been experimentally evaluated on movie review dataset. The results show that the proposed method is computationally efficient and in exchange sacrifices only a small amount of accuracy.

[1]  Andrew G. Clark,et al.  Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL) , 2002 .

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

[3]  Hua Xu,et al.  An empirical study of unsupervised sentiment classification of Chinese reviews , 2010 .

[4]  Jun Guo,et al.  Exploiting Combined Multi-level Model for Document Sentiment Analysis , 2010, 2010 20th International Conference on Pattern Recognition.

[5]  Rui Xia,et al.  Ensemble of feature sets and classification algorithms for sentiment classification , 2011, Inf. Sci..

[6]  Fei Song,et al.  Comparison of Feature Selection Methods for Sentiment Analysis , 2010, Canadian Conference on AI.

[7]  Qinke Peng,et al.  Sentiment Analysis for Product Features in Chinese Reviews Based on Semantic Association , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[8]  Miles James Efron.  Cultural Orientation: Classifying Subjective Documents by Cociation Analysis , 2004, AAAI Technical Report.

[9]  Hsinchun Chen,et al.  Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums , 2008, TOIS.

[10]  M. de Rijke,et al.  UvA-DARE ( Digital Academic Repository ) Using WordNet to measure semantic orientations of adjectives , 2004 .

[11]  Claire Cardie,et al.  Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns , 2005, HLT.

[12]  W. Scott Spangler,et al.  Leveraging Sentiment Analysis for Topic Detection , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[13]  Mitsuru Ishizuka,et al.  SentiFul: Generating a reliable lexicon for sentiment analysis , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[14]  Yi Hu,et al.  Document sentiment classification by exploring description model of topical terms , 2011, Comput. Speech Lang..

[15]  B WhinstonAndrew,et al.  Do online reviews matter? - An empirical investigation of panel data , 2008 .

[16]  Rudy Prabowo,et al.  Sentiment analysis: A combined approach , 2009, J. Informetrics.

[17]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

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

[19]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[20]  Khurshid Ahmad,et al.  Visualising sentiments in financial texts? , 2005, Ninth International Conference on Information Visualisation (IV'05).

[21]  Son Bao Pham,et al.  Sentiment Analysis for Vietnamese , 2010, 2010 Second International Conference on Knowledge and Systems Engineering.

[22]  Jian Zhu,et al.  Sentiment classification using the theory of ANNs , 2010 .

[23]  Jonathon Read,et al.  Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification , 2005, ACL.

[24]  Q. Ye,et al.  The impact of e-word-of-mouth on the online popularity of restaurants: a comparison of consumer reviews and editor reviews. , 2010 .

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

[26]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[27]  Ruwei Dai,et al.  AMAZING: A sentiment mining and retrieval system , 2009, Expert Syst. Appl..

[28]  Bing Qin,et al.  Integrating Intra-and Inter-document Evidences for Improving Sentence Sentiment Classification , 2010 .

[29]  Xue Bai,et al.  Predicting consumer sentiments from online text , 2011, Decis. Support Syst..

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

[31]  Desheng Dash Wu,et al.  Using text mining and sentiment analysis for online forums hotspot detection and forecast , 2010, Decis. Support Syst..

[32]  F. Scientific.  Online Shopping , 1998, Current Biology.

[33]  Hsinchun Chen,et al.  Selecting Attributes for Sentiment Classification Using Feature Relation Networks , 2011, IEEE Transactions on Knowledge and Data Engineering.

[34]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[35]  Ingoo Han,et al.  The effect of negative online consumer reviews on product attitude: An information processing view , 2008, Electron. Commer. Res. Appl..

[36]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[37]  Soo-Min Kim,et al.  Automatic Detection of Opinion Bearing Words and Sentences , 2005, IJCNLP.

[38]  George Forman,et al.  An Extensive Empirical Study of Feature Selection Metrics for Text Classification , 2003, J. Mach. Learn. Res..

[39]  Jin Zhang,et al.  An empirical study of sentiment analysis for chinese documents , 2008, Expert Syst. Appl..

[40]  Bin Gu,et al.  Do online reviews matter? - An empirical investigation of panel data , 2008, Decis. Support Syst..

[41]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.