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What is the Conversation About?: A Topic-Model-Based Approach for Analyzing Customer Sentiments in Twitter

What is the Conversation About?: A Topic-Model-Based Approach for Analyzing Customer Sentiments in Twitter

Stefan Sommer, Andreas Schieber, Kai Heinrich, Andreas Hilbert
Copyright: © 2012 |Volume: 8 |Issue: 1 |Pages: 16
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781466612990|DOI: 10.4018/jiit.2012010102
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MLA

Sommer, Stefan, et al. "What is the Conversation About?: A Topic-Model-Based Approach for Analyzing Customer Sentiments in Twitter." IJIIT vol.8, no.1 2012: pp.10-25. http://doi.org/10.4018/jiit.2012010102

APA

Sommer, S., Schieber, A., Heinrich, K., & Hilbert, A. (2012). What is the Conversation About?: A Topic-Model-Based Approach for Analyzing Customer Sentiments in Twitter. International Journal of Intelligent Information Technologies (IJIIT), 8(1), 10-25. http://doi.org/10.4018/jiit.2012010102

Chicago

Sommer, Stefan, et al. "What is the Conversation About?: A Topic-Model-Based Approach for Analyzing Customer Sentiments in Twitter," International Journal of Intelligent Information Technologies (IJIIT) 8, no.1: 10-25. http://doi.org/10.4018/jiit.2012010102

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Abstract

In Social Commerce customers evolve to be an important information source for companies. Customers use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments about products or discuss their experiences with them. These sentiments can be very important for the development of products or the enhancement of marketing strategies. The research goal is to analyze customer sentiments in Twitter. The first step in the research is the detection of topics in Twitter entries which contain patterns of interest. For the topic detection, the authors use Latent Dirichlet Allocation for topic modeling. The authors found event based topics in the exemplary context of Sony’s 3D TV sets. In future work, the authors will implement sentiment analysis algorithms in order to determine sentiments in the entries corresponding to the detected topics.

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