What Users Prefer and Why: A User Study on Effective Presentation Styles of Opinion Summarization

Opinion Summarization research addresses how to help people in making appropriate decisions in an effective way. This paper aims to help users in their decision-making by providing them effective opinion presentation styles. We carried out two phases of experiments to systematically compare usefulness of different types of opinion summarization techniques. In the first crowd-sourced study, we recruited 46 turkers to generate high quality summary information. This first phase generated four styles of summaries: Tag Clouds, Aspect Oriented Sentiments, Paragraph Summary and Group Sample. In the follow-up second phase, 34 participants tested the four styles in a card sorting experiment. Each participant was given 32 cards with 8 per presentation styles and completed the task of grouping the cards into five categories in terms of the usefulness of the cards. Results indicated that participants preferred Aspect Oriented Sentiments the most and Tag cloud the least. Implications and hypotheses are discussed.

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

[2]  Arjun Mukherjee,et al.  Aspect Extraction through Semi-Supervised Modeling , 2012, ACL.

[3]  Marti A. Hearst,et al.  Reexamining the cluster hypothesis: scatter/gather on retrieval results , 1996, SIGIR '96.

[4]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[5]  Ross Wilkinson,et al.  Using clustering and classification approaches in interactive retrieval , 2001, Inf. Process. Manag..

[6]  Dragomir R. Radev,et al.  LexRank: Graph-based Lexical Centrality as Salience in Text Summarization , 2004, J. Artif. Intell. Res..

[7]  Kathleen R. McKeown,et al.  Generating natural language summaries from multiple on-line sources , 1998 .

[8]  Yue Lu,et al.  Rated aspect summarization of short comments , 2009, WWW '09.

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

[10]  Sasha Blair-Goldensohn,et al.  Sentiment Summarization: Evaluating and Learning User Preferences , 2009, EACL.

[11]  Anton Leuski,et al.  Evaluating document clustering for interactive information retrieval , 2001, CIKM '01.

[12]  Lei Shi,et al.  VISA: a visual sentiment analysis system , 2012, VINCI.

[13]  Jackie Chi Kit Cheung,et al.  Multi-Document Summarization of Evaluative Text , 2013, EACL.

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

[15]  Yue Lu,et al.  Opinion integration through semi-supervised topic modeling , 2008, WWW.

[16]  Mary S. Neff,et al.  Multi-document Summarization by Visualizing Topical Content , 2000 .

[17]  Steffen Becker,et al.  Opinion Summarization of Web Comments , 2010, ECIR.

[18]  Eben M. Haber,et al.  OpinionBlocks: A Crowd-Powered, Self-improving Interactive Visual Analytic System for Understanding Opinion Text , 2013, INTERACT.

[19]  Hsin-Hsi Chen,et al.  Opinion Extraction, Summarization and Tracking in News and Blog Corpora , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[20]  Christopher H. Brooks,et al.  Improved annotation of the blogosphere via autotagging and hierarchical clustering , 2006, WWW '06.

[21]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

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

[23]  Koji Yatani,et al.  Review spotlight: a user interface for summarizing user-generated reviews using adjective-noun word pairs , 2011, CHI.

[24]  Eduard H. Hovy,et al.  Automated Text Summarization and the SUMMARIST System , 1998, TIPSTER.

[25]  Inderjeet Mani,et al.  The Tipster Summac Text Summarization Evaluation , 1999, EACL.

[26]  Carl Gutwin,et al.  Seeing things in the clouds: the effect of visual features on tag cloud selections , 2008, Hypertext.

[27]  R. G. Raj,et al.  A Preliminary Investigation of User Perception and Behavioral Intention for Different Review Types: Customers and Designers Perspective , 2014, TheScientificWorldJournal.

[28]  Ryan T. McDonald,et al.  Contrastive Summarization: An Experiment with Consumer Reviews , 2009, NAACL.

[29]  Manuel de Buenaga,et al.  Multidocument summarization: An added value to clustering in interactive retrieval , 2004 .

[30]  Chaomei Chen,et al.  Visual Analysis of Conflicting Opinions , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.

[31]  Giuseppe Carenini,et al.  Interactive multimedia summaries of evaluative text , 2006, IUI '06.