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Proceedings Paper

Integrating sentiment analysis and term associations with geo-temporal visualizations on customer feedback streams
Author(s): Ming Hao; Christian Rohrdantz; Halldór Janetzko; Daniel Keim; Umeshwar Dayal; Lars-Erik Haug; Mei-Chun Hsu
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Paper Abstract

Twitter currently receives over 190 million tweets (small text-based Web posts) and manufacturing companies receive over 10 thousand web product surveys a day, in which people share their thoughts regarding a wide range of products and their features. A large number of tweets and customer surveys include opinions about products and services. However, with Twitter being a relatively new phenomenon, these tweets are underutilized as a source for determining customer sentiments. To explore high-volume customer feedback streams, we integrate three time series-based visual analysis techniques: (1) feature-based sentiment analysis that extracts, measures, and maps customer feedback; (2) a novel idea of term associations that identify attributes, verbs, and adjectives frequently occurring together; and (3) new pixel cell-based sentiment calendars, geo-temporal map visualizations and self-organizing maps to identify co-occurring and influential opinions. We have combined these techniques into a well-fitted solution for an effective analysis of large customer feedback streams such as for movie reviews (e.g., Kung-Fu Panda) or web surveys (buyers).

Paper Details

Date Published: 24 January 2012
PDF: 12 pages
Proc. SPIE 8294, Visualization and Data Analysis 2012, 82940H (24 January 2012); doi: 10.1117/12.912202
Show Author Affiliations
Ming Hao, Hewlett-Packard Labs. (United States)
Christian Rohrdantz, Univ. of Konstanz (Germany)
Halldór Janetzko, Univ. of Konstanz (Germany)
Daniel Keim, Univ. of Konstanz (Germany)
Umeshwar Dayal, Hewlett-Packard Labs. (United States)
Lars-Erik Haug, Hewlett-Packard Labs. (United States)
Mei-Chun Hsu, Hewlett-Packard Labs. (United States)


Published in SPIE Proceedings Vol. 8294:
Visualization and Data Analysis 2012
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen; Robert Kosara; Mark A. Livingston; Jinah Park; Ian Roberts, Editor(s)

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