Authors:
Shiaofen Fang
1
;
Lanfang Miao
2
and
Eric Lin
1
Affiliations:
1
Indiana University Purdue University Indianapolis, United States
;
2
Zhejinag Normal University, China
Keyword(s):
Visual Clustering, Online Reviews, Text Mining, Social Network.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
Databases and Visualization, Visual Data Mining
;
General Data Visualization
;
Information and Scientific Visualization
;
Internet, Web and Security Visualization
;
Text and Document Visualization
Abstract:
Online user reviews of products, movies, books, etc. have been an important source of information for applications such as social networking, online retail, and sentiment analysis. In this paper, we present a novel visualization tool for analysing and visualizing online book reviews. Using text mining techniques, nontrivial features (tags) are identified on the text data extracted from the online reviews. These keyword tags are used to cluster both the books and the readers based on global tag similarities. Two different visualization methods are proposed: parallel coordinate views and 3D correlative cluster views. The parallel coordinate visualization provides a flat view of the tag distributions to reveal clustering patterns. A novel 3D corrective visualization technique is developed to visually represent the correlations of reader clusters and book clusters. These visualization techniques can also be applied to other types of online text data in social networks and web commerce.