Visual Sentiment Summarization of Movie Reviews

A prototype digital library of social media content was developed to present a summarized view of public opinion in a visual interface. The domain of the study was movie reviews of multiple genres harvested from weblogs, discussion boards, user and critic review Web sites, and Twitter. The system performs fine-grained analysis to determine both the sentiment orientation and sentiment strength of the reviewer towards various aspects of a movie, such as overall opinion, director, cast, story, scene, and music. Various visual interface components were developed to present an overview of public opinion on multiple aspects of each movie, and a usability evaluation was conducted to observe their effectiveness. Aspect-based sentiment summarization interface has the highest score for usefulness while a sentiment link analysis graph visualizing how positive and negative sentiment terms are associated with review aspects has the highest score for overall rating.

[1]  Christopher S. G. Khoo,et al.  Aspect-based sentiment analysis of movie reviews on discussion boards , 2010, J. Inf. Sci..

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

[3]  Satoshi Morinaga,et al.  Mining product reputations on the Web , 2002, KDD.

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

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

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

[7]  Sandra Payette,et al.  The Fedora Project: An Open-source Digital Object Repository Management System , 2003, D Lib Mag..

[8]  Philip S. Yu,et al.  A holistic lexicon-based approach to opinion mining , 2008, WSDM '08.

[9]  Kathleen R. McKeown,et al.  Predicting the semantic orientation of adjectives , 1997 .

[10]  Mitsuru Ishizuka,et al.  SENTIMENT ASSESSMENT OF TEXT BY ANALYZING LINGUISTIC FEATURES AND CONTEXTUAL VALENCE ASSIGNMENT , 2008, Appl. Artif. Intell..

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

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

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

[14]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[15]  Andrea Esuli,et al.  Determining Term Subjectivity and Term Orientation for Opinion Mining , 2006, EACL.