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Visual analysis of conflicting opinions
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/1849
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| 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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