When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging

This study presents a novel approach to the problem of system portability across different domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on WordNet. The paper explores the challenges of system portability across domains and text genres (movie reviews, news, blogs, and product reviews), highlights the factors affecting system performance on out-of-domain and smallset in-domain data, and presents a new system consisting of the ensemble of two classifiers with precision-based vote weighting, that provides significant gains in accuracy and recall over the corpus-based classifier and the lexicon-based system taken individually.

[1]  Vibhu O. Mittal,et al.  Comparative Experiments on Sentiment Classification for Online Product Reviews , 2006, AAAI.

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

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

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

[5]  Michael Gamon,et al.  Automatic Identification of Sentiment Vocabulary: Exploiting Low Association with Known Sentiment Terms , 2005, ACL 2005.

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

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

[8]  Philip J. Stone,et al.  Extracting Information. (Book Reviews: The General Inquirer. A Computer Approach to Content Analysis) , 1967 .

[9]  Sabine Bergler,et al.  Mining WordNet for a Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses , 2006, EACL.

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

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

[12]  Alistair Kennedy,et al.  SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS , 2006, Comput. Intell..

[13]  Songbo Tan,et al.  A novel scheme for domain-transfer problem in the context of sentiment analysis , 2007, CIKM '07.

[14]  Carlo Strapparava,et al.  SemEval-2007 Task 14: Affective Text , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

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

[16]  L. Zadeh.  Calculus of fuzzy restrictions , 1996 .

[17]  Edoardo M. Airoldi,et al.  On Learning Parsimonious Models for Extracting Consumer Opinions , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

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

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

[20]  Marshall S. Smith,et al.  The general inquirer: A computer approach to content analysis. , 1967 .

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

[22]  Janyce Wiebe,et al.  A Corpus Study of Evaluative and Speculative Language , 2001, SIGDIAL Workshop.

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

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