Files and links (1)
SubmittedOpen Access (License Unspecified), Open
Conference proceeding
Visual analysis of conflicting opinions
VAST 2006: IEEE SYMPOSIUM ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY, PROCEEDINGS, pp 59-66
01 Jan 2006
Abstract
Understanding the nature and dynamics of conflicting opinions is a profound and challenging issue. In this paper we address several aspects of the issue through a study of more than 3,000 Amazon customer reviews of the controversial bestseller The Da Vinci Code, including 1,738 positive and 918 negative reviews. The study is motivated by critical questions such as: What are the differences between positive and negative reviews? What is the origin of a particular opinion? How do these opinions change over time? To what extent can differentiating features be identified from unstructured text? How accurately can these features predict the category of a review? We first analyze terminology variations in these reviews in terms of syntactic, semantic, and statistic associations identified by TermWatch and use term variation patterns to depict underlying topics. We then select the most predictive terms based on log likelihood tests and demonstrate that this small set of terms classifies over 70% of the conflicting reviews correctly. This feature selection process reduces the dimensionality of the feature space from more than 20,000 dimensions to a couple of hundreds. We utilize automatically generated decision trees to facilitate the understanding of conflicting opinions in terms of these highly predictive terms. This study also uses a number of visualization and modeling tools to identify not only what positive and negative reviews have in common. but also they differ and evolve over time.
Metrics
Details
- Title
- Visual analysis of conflicting opinions
- Creators
- Chaomei Chen - Drexel UniversityFidelia Ibekwe-SanJuan - University of Lyon SystemEric SanJuan - University of AvignonChris Weaver - Pennsylvania State University
- Contributors
- P C Wong (Editor)
- Publication Details
- VAST 2006: IEEE SYMPOSIUM ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY, PROCEEDINGS, pp 59-66
- Series
- IEEE Conference on Visual Analytics Science and Technology
- Publisher
- IEEE
- Number of pages
- 2
- Grant note
- Northeast Visualization and Analytics Center (NEVAC)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000243470000008
- Scopus ID
- 2-s2.0-36349011694
- Other Identifier
- 991019173578104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering