A unified relevance model for opinion retrieval

Representing the information need is the greatest challenge for opinion retrieval. Typical queries for opinion retrieval are composed of either just content words, or content words with a small number of cue "opinion" words. Both are inadequate for retrieving opinionated documents. In this paper, we develop a general formal framework--the opinion relevance model--to represent an information need for opinion retrieval. We explore a series of methods to automatically identify the most appropriate opinion words for query expansion, including using query independent sentiment resources. We also propose a relevance feedback-based approach to extract opinion words. Both query-independent and query-dependent methods can also be integrated into a more effective mixture relevance model. Finally, opinion retrieval experiments are presented for the Blog06 and COAE08 text collections. The results show that, significant improvements can always be obtained by this opinion relevance model whether sentiment resources are available or not.

[1]  Craig MacDonald,et al.  On the TREC Blog Track , 2021, ICWSM.

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

[3]  W. Bruce Croft,et al.  Search Engines - Information Retrieval in Practice , 2009 .

[4]  CHENGXIANG ZHAI,et al.  A study of smoothing methods for language models applied to information retrieval , 2004, TOIS.

[5]  Min Zhang,et al.  A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval , 2008, SIGIR '08.

[6]  Tie-Yan Liu,et al.  Learning to rank for information retrieval (LR4IR 2007) , 2007, SIGF.

[7]  Wei Zhang,et al.  UIC at TREC 2007 Blog Track , 2007, TREC.

[8]  Clement Yu,et al.  UIC at TREC 2008 Blog Track , 2008 .

[9]  Claire Cardie,et al.  Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.

[10]  Iadh Ounis,et al.  The TREC Blogs06 Collection: Creating and Analysing a Blog Test Collection , 2006 .

[11]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[12]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[13]  Giorgio Gambosi,et al.  FUB, IASI-CNR and University of Tor Vergata at TREC 2008 Blog Track , 2008, TREC.

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

[15]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[16]  Craig MacDonald,et al.  University of Glasgow at TREC 2007: Experiments in Blog and Enterprise Tracks with Terrier , 2007, TREC.

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

[18]  Koji Eguchi,et al.  Sentiment Retrieval using Generative Models , 2006, EMNLP.

[19]  Coskun Bayrak,et al.  Topic Categorization for Relevancy and Opinion Detection , 2007, TREC.

[20]  Jimmy J. Lin,et al.  TREC 2006 at Maryland: Blog, Enterprise, Legal and QA Tracks , 2006, TREC.

[21]  Steve Renals,et al.  Proceedings of the Ninth Text REtrieval Conference , 2001 .

[22]  Jong-Hyeok Lee,et al.  Improving Opinion Retrieval Based on Query-Specific Sentiment Lexicon , 2009, ECIR.

[23]  Stephen E. Robertson,et al.  Optimisation methods for ranking functions with multiple parameters , 2006, CIKM '06.

[24]  Iadh Ounis,et al.  University of Glasgow at TREC 2006: Experiments in Terabyte and Enterprise Tracks with Terrier , 2006, TREC.

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

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

[27]  Olga Vechtomova.  Using Subjective Adjectives in Opinion Retrieval from Blogs , 2007, TREC.

[28]  Craig MacDonald,et al.  Overview of the TREC 2007 Blog Track , 2007, TREC.

[29]  Iadh Ounis,et al.  Overview of the TREC 2008 Blog Track , 2008, TREC.

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