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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/1849

Title: Visual analysis of conflicting opinions
Authors: Chen, Chaomei
Ibekwe-SanJuan, Fidelia
San Juan, Eric
Weaver, Chris
Keywords: Visual Analytics;Conflicting Opinions;Terminology Variation;Decision Tree;Predictive Text Analysis;Sense Making
Issue Date: Oct-2006
Citation: Chaomei, C., Fidelia, I.-S., Eric, S., & Chris, W. (2006). Visual Analysis of Conflicting Opinions, 2006 IEEE Symposium on Visual Analytics Science and Technology, pp. 59-66.
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.
URI: http://hdl.handle.net/1860/1849
Appears in Collections:Faculty Research and Publications (IST)

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