Cross-Domain Sentiment Classification via Polarity-Driven State Transitions in a Markov Model

Nowadays understanding people’s opinions is the way to success, whatever the goal. Sentiment classification automates this task, assigning a positive, negative or neutral polarity to free text concerning services, products, TV programs, and so on. Learning accurate models requires a considerable effort from human experts that have to properly label text data. To reduce this burden, cross-domain approaches are advisable in real cases and transfer learning between source and target domains is usually demanded due to language heterogeneity. This paper introduces some variants of our previous work [1], where both transfer learning and sentiment classification are performed by means of a Markov model. While document splitting into sentences does not perform well on common benchmark, using polarity-bearing terms to drive the classification process shows encouraging results, given that our Markov model only considers single terms without further context information.

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

[2]  Liwen Qiu,et al.  Markov Models of Search State Patterns in a Hypertext Information Retrieval System , 1993, J. Am. Soc. Inf. Sci..

[3]  Prem Melville,et al.  Sentiment analysis of blogs by combining lexical knowledge with text classification , 2009, KDD.

[4]  Jamshid Beheshti,et al.  A Text Categorization Model Based on Hidden Markov Models , 2013 .

[5]  Lin-Shan Lee,et al.  Interactive Spoken Document Retrieval With Suggested Key Terms Ranked by a Markov Decision Process , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

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

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

[8]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[9]  Michael Gamon,et al.  Customizing Sentiment Classifiers to New Domains: a Case Study , 2019 .

[10]  Alexei A. Efros,et al.  Data-driven visual similarity for cross-domain image matching , 2011, ACM Trans. Graph..

[11]  Qiang Yang,et al.  Co-clustering based classification for out-of-domain documents , 2007, KDD '07.

[12]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[13]  Tao Dong,et al.  Text Categorization Based on Semantic Cluster-Hidden Markov Models , 2013, ICSI.

[14]  Giovanni Soda,et al.  Hidden Markov Models for Text Categorization in Multi-Page Documents , 2002, Journal of Intelligent Information Systems.

[15]  Claudio Sartori,et al.  Cross-domain Text Classification through Iterative Refining of Target Categories Representations , 2014, KDIR.

[16]  Qiang Yang,et al.  Topic-bridged PLSA for cross-domain text classification , 2008, SIGIR '08.

[17]  Peter Schäuble,et al.  Document and passage retrieval based on hidden Markov models , 1994, SIGIR '94.

[18]  Mingsheng Long,et al.  Topic Correlation Analysis for Cross-Domain Text Classification , 2012, AAAI.

[19]  Mike Thelwall,et al.  A Study of Information Retrieval Weighting Schemes for Sentiment Analysis , 2010, ACL.

[20]  Xiaodong Gu,et al.  Reducing Over-Weighting in Supervised Term Weighting for Sentiment Analysis , 2014, COLING.

[21]  Ramesh R. Sarukkai,et al.  Link prediction and path analysis using Markov chains , 2000, Comput. Networks.

[22]  Songbo Tan,et al.  Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples , 2008, SIGIR '08.

[23]  M. F. Porter,et al.  An algorithm for suffix stripping , 1997 .

[24]  Richard M. Schwartz,et al.  A hidden Markov model information retrieval system , 1999, SIGIR '99.

[25]  Marco Masseroli,et al.  Cross-organism learning method to discover new gene functionalities , 2016, Comput. Methods Programs Biomed..

[26]  Claudio Sartori,et al.  Iterative Refining of Category Profiles for Nearest Centroid Cross-Domain Text Classification , 2014, IC3K.

[27]  Yang Huang,et al.  Combining Text Classification and Hidden Markov Modeling Techniques for Structuring Randomized Clinical Trial Abstracts , 2006, AMIA.

[28]  Danushka Bollegala,et al.  Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus , 2013, IEEE Transactions on Knowledge and Data Engineering.

[29]  Jamshid Beheshti,et al.  A hidden Markov model-based text classification of medical documents , 2009, J. Inf. Sci..

[30]  Claudio Sartori,et al.  A Study on Term Weighting for Text Categorization: A Novel Supervised Variant of tf.idf , 2015, DATA.

[31]  Jeonghee Yi,et al.  Sentiment analysis: capturing favorability using natural language processing , 2003, K-CAP '03.

[32]  Marco Masseroli,et al.  Random Perturbations of Term Weighted Gene Ontology Annotations for Discovering Gene Unknown Functionalities , 2014, IC3K.

[33]  Hongliang Yu,et al.  A study of supervised term weighting scheme for sentiment analysis , 2014, Expert Syst. Appl..

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

[35]  Likun Qiu,et al.  SELC: a self-supervised model for sentiment classification , 2009, CIKM.

[36]  Jinxi Xu,et al.  Cross-lingual Information Retrieval Using Hidden Markov Models , 2000, EMNLP.

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

[38]  Rohini K. Srihari,et al.  OpinionMiner: a novel machine learning system for web opinion mining and extraction , 2009, KDD.

[39]  Giacomo Domeniconi,et al.  Markov chain based method for in-domain and cross-domain sentiment classification , 2015, 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K).

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

[41]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[42]  Claudio Sartori,et al.  A Comparison of Term Weighting Schemes for Text Classification and Sentiment Analysis with a Supervised Variant of tf.idf , 2015, DATA.

[43]  Qiang Yang,et al.  Cross-domain sentiment classification via spectral feature alignment , 2010, WWW '10.

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

[45]  Jian-Yun Nie,et al.  Using Markov Chains to Exploit Word Relationships in Information Retrieval , 2007, RIAO.