Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment Analysis

AbstractAn emerging field within sentiment analysis concerns the investigation about how sentiment polarities associated with concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset. The results demonstrate its viability in real-world cases.

[1]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[2]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[3]  W. Dong,et al.  Fuzzy computations in risk and decision analysis , 1985 .

[4]  H. Hellendoorn,et al.  Defuzzification in Fuzzy Controllers , 1993, J. Intell. Fuzzy Syst..

[5]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[6]  Andrew McCallum,et al.  Information Extraction with HMM Structures Learned by Stochastic Optimization , 2000, AAAI/IAAI.

[7]  Janyce Wiebe,et al.  Effects of Adjective Orientation and Gradability on Sentence Subjectivity , 2000, COLING.

[8]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[9]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

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

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

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

[13]  Hong Yu,et al.  Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences , 2003, EMNLP.

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

[15]  Janyce Wiebe,et al.  Just How Mad Are You? Finding Strong and Weak Opinion Clauses , 2004, AAAI.

[16]  Janyce Wiebe,et al.  Learning Subjective Language , 2004, CL.

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

[18]  Luo Si,et al.  Knowledge Transfer and Opinion Detection in the TREC2006 Blog Track , 2006 .

[19]  Soo-Min Kim,et al.  Automatically Assessing Review Helpfulness , 2006, EMNLP.

[20]  Janyce Wiebe,et al.  RECOGNIZING STRONG AND WEAK OPINION CLAUSES , 2006, Comput. Intell..

[21]  Grace Hui Yang,et al.  Knowledge Transfer and Opinion Detection in the TREC 2006 Blog Track , 2006, TREC.

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

[23]  Soo-Min Kim,et al.  Crystal: Analyzing Predictive Opinions on the Web , 2007, EMNLP.

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

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

[26]  Xinying Xu,et al.  Hidden sentiment association in chinese web opinion mining , 2008, WWW.

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

[28]  Nicholas Asher,et al.  Distilling Opinion in Discourse: A Preliminary Study , 2008, COLING.

[29]  Marco Grassi.  Developing HEO Human Emotions Ontology , 2009, COST 2101/2102 Conference.

[30]  B. Liu,et al.  Expanding Domain Sentiment Lexicon through Double Propagation , 2009, IJCAI.

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

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

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

[34]  Xuanjing Huang,et al.  Phrase Dependency Parsing for Opinion Mining , 2009, EMNLP.

[35]  J. Wiebe,et al.  Discourse-level relations for opinion analysis , 2010 .

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

[37]  Iryna Gurevych,et al.  Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields , 2010, EMNLP.

[38]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[39]  Guodong Zhou,et al.  Topic-Driven Multi-document Summarization , 2010, 2010 International Conference on Asian Language Processing.

[40]  Junlan Feng,et al.  Robust Sentiment Detection on Twitter from Biased and Noisy Data , 2010, COLING.

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

[42]  Alan F. Smeaton,et al.  Classifying sentiment in microblogs: is brevity an advantage? , 2010, CIKM.

[43]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[44]  Chun Chen,et al.  Opinion Word Expansion and Target Extraction through Double Propagation , 2011, CL.

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

[46]  Yuji Matsumoto,et al.  Transfer Learning for Multiple-Domain Sentiment Analysis - Identifying Domain Dependent/Independent Word Polarity , 2011, AAAI.

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

[48]  Erik Cambria,et al.  Sentic Computing: Techniques, Tools, and Applications , 2012 .

[49]  Wiltrud Kessler.  Turney: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classication of Reviews , 2012 .

[50]  Charu C. Aggarwal,et al.  Mining Text Data , 2012 .

[51]  Erik Cambria,et al.  Sentic Album: Content-, Concept-, and Context-Based Online Personal Photo Management System , 2012, Cognitive Computation.

[52]  Erik Cambria,et al.  Common Sense Knowledge for Handwritten Chinese Text Recognition , 2013, Cognitive Computation.

[53]  Mike Thelwall,et al.  Semi-supervised vs. Cross-domain Graphs for Sentiment Analysis , 2013, RANLP.

[54]  Jane Yung-jen Hsu,et al.  Building a Concept-Level Sentiment Dictionary Based on Commonsense Knowledge , 2013, IEEE Intelligent Systems.

[55]  Rui Xia,et al.  Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification , 2013, IEEE Intelligent Systems.

[56]  Hung-Yu Kao,et al.  Automatic Domain-Specific Sentiment Lexicon Generation with Label Propagation , 2013, IIWAS '13.

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

[58]  Erik Cambria,et al.  SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis , 2014, AAAI.

[59]  Zhendong Niu,et al.  Automatic construction of domain-specific sentiment lexicon based on constrained label propagation , 2014, Knowl. Based Syst..

[60]  Kiran Bhowmick,et al.  A Survey of Opinion Mining and Sentiment Analysis , 2015 .