Paper Info
Title
Visual opinion analysis of customer feedback data
Abstract
Today, online stores collect a lot of customer feedback in the form of surveys, reviews, and comments. This feedback is categorized and in some cases responded to, but in general it is underutilized - even though customer satisfaction is essential to the success of their business. In this paper, we introduce several new techniques to interactively analyze customer comments and ratings to determine the positive and negative opinions expressed by the customers. First, we introduce a new discrimination-based technique to automatically extract the terms that are the subject of the positive or negative opinion (such as price or customer service) and that are frequently commented on. Second, we derive a Reverse-Distance-Weighting method to map the attributes to the related positive and negative opinions in the text. Third, the resulting high-dimensional feature vectors are visualized in a new summary representation that provides a quick overview. We also cluster the reviews according to the similarity of the comments. Special thumbnails are used to provide insight into the composition of the clusters and their relationship. In addition, an interactive circular correlation map is provided to allow analysts to detect the relationships of the comments to other important attributes and the scores. We have applied these techniques to customer comments from real-world online stores and product reviews from web sites to identify the strength and problems of different products and services, and show the potential of our technique.
Year
DOI
Venue
2009
10.1109/VAST.2009.5333919
IEEE VAST
Keywords
Field
DocType
visual document analysis,attribute extraction index terms: i.7.5 document and text processing: document,visual sentiment analysis,visual opinion analysis,customer satisfaction,data mining,data visualisation,sentiment analysis,correlation,feature vector,indexing terms,internet,feature extraction,visualization
Thumbnail,Customer intelligence,Data science,Data mining,Data visualization,Feature vector,Customer satisfaction,Voice of the customer,Computer science,Customer reference program,The Internet
Conference
Citations 
PageRank 
References 
38
1.56
14
Authors
7
Name
Order
Citations
PageRank
Daniela Oelke122513.18
Ming C. Hao2814.59
Christian Rohrdantz320513.86
Daniel A. Keim477041141.60
Umeshwar Dayal584522538.92
Lars-Erik Haug6874.26
Halldor Janetzko731220.69