Capturing the Stars: Predicting Ratings for Service and Product Reviews

Bloggers, professional reviewers, and consumers continuously create opinion--rich web reviews about products and services, with the result that textual reviews are now abundant on the web and often convey a useful overall rating (number of stars). However, an overall rating cannot express the multiple or conflicting opinions that might be contained in the text, or explicitly rate the different aspects of the evaluated entity. This work addresses the task of automatically predicting ratings, for given aspects of a textual review, by assigning a numerical score to each evaluated aspect in the reviews. We handle this task as both a regression and a classification modeling problem and explore several combinations of syntactic and semantic features. Our results suggest that classification techniques perform better than ranking modeling when handling evaluative text.

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

[2]  Ling Li,et al.  Ordinal Regression by Extended Binary Classification , 2006, NIPS.

[3]  Koby Crammer,et al.  Pranking with Ranking , 2001, NIPS.

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

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

[6]  Kazutaka Shimada,et al.  Seeing Several Stars: A Rating Inference Task for a Document Containing Several Evaluation Criteria , 2008, PAKDD.

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

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

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

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

[11]  Wayne Niblack,et al.  Sentiment mining in WebFountain , 2005, 21st International Conference on Data Engineering (ICDE'05).

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

[13]  Regina Barzilay,et al.  Multiple Aspect Ranking Using the Good Grief Algorithm , 2007, NAACL.

[14]  Eibe Frank,et al.  A Simple Approach to Ordinal Classification , 2001, ECML.

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

[16]  Jun'ichi Tsujii,et al.  Assigning Polarity Scores to Reviews Using Machine Learning Techniques , 2005, IJCNLP.

[17]  Wei Chu,et al.  New approaches to support vector ordinal regression , 2005, ICML.

[18]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.