Paper Info
Title
Visual Sentiment Analysis Of Customer Feedback Streams Using Geo-Temporal Term Associations
Abstract
Large manufacturing companies frequently receive thousands of web surveys every day. People share their thoughts regarding a wide range of products, their features, and the service they received. In addition, more than 190 million tweets (small text Web posts) are generated daily. Both survey feedback and tweets are underutilized as a source for understanding customer sentiments. To explore high-volume customer feedback streams, in this article, we introduce four time series visual analysis techniques: (1) feature-based sentiment analysis that extracts, measures, and maps customer feedback; (2) a novel way of determining term associations that identify attributes, verbs, and adjectives frequently occurring together; (3) a self-organizing term association map and a pixel cell-based sentiment calendar to identify co-occurring and influential opinion; and (4) a new geo-based term association technique providing a key term geo map to enable the user to inspect the statistical significance and the sentiment distribution of individual key terms. We have used and evaluated these techniques and combined them into a well-fitted solution for an effective analysis of large customer feedback streams such as web surveys (from product buyers) and Twitter (e.g. from Kung-Fu Panda movie reviewers).
Year
DOI
Venue
2013
10.1177/1473871613481691
INFORMATION VISUALIZATION
Keywords
Field
DocType
Customer sentiment visual analytics, term association, geo-term association, pixel geo map, key term geo map, pixel calendar
Customer feedback,Information retrieval,Computer science,Sentiment analysis,Pixel
Journal
Volume
Issue
ISSN
12
3-4
1473-8716
Citations 
PageRank 
References 
9
0.56
17
Authors
8
Name
Order
Citations
PageRank
Ming C. Hao1814.59
Christian Rohrdantz220513.86
Halldor Janetzko331220.69
Daniel A. Keim477041141.60
Umeshwar Dayal584522538.92
Lars-Erik Haug6874.26
Meichun Hsu73437778.34
Florian Stoffel81069.38