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Evaluation of Topic Models as a Preprocessing Engine for the Knowledge Discovery in Twitter Datasets

Evaluation of Topic Models as a Preprocessing Engine for the Knowledge Discovery in Twitter Datasets

Stefan Sommer, Tom Miller, Andreas Hilbert
Copyright: © 2016 |Pages: 17
ISBN13: 9781466698406|ISBN10: 1466698403|EISBN13: 9781466698413
DOI: 10.4018/978-1-4666-9840-6.ch057
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MLA

Sommer, Stefan, et al. "Evaluation of Topic Models as a Preprocessing Engine for the Knowledge Discovery in Twitter Datasets." Big Data: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 1260-1276. https://doi.org/10.4018/978-1-4666-9840-6.ch057

APA

Sommer, S., Miller, T., & Hilbert, A. (2016). Evaluation of Topic Models as a Preprocessing Engine for the Knowledge Discovery in Twitter Datasets. In I. Management Association (Ed.), Big Data: Concepts, Methodologies, Tools, and Applications (pp. 1260-1276). IGI Global. https://doi.org/10.4018/978-1-4666-9840-6.ch057

Chicago

Sommer, Stefan, Tom Miller, and Andreas Hilbert. "Evaluation of Topic Models as a Preprocessing Engine for the Knowledge Discovery in Twitter Datasets." In Big Data: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1260-1276. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9840-6.ch057

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Abstract

In the World Wide Web, users are an important information source for companies or institutions. People use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments of products, politics, society, or even private situations. In 2014, the Twitter users worldwide submitted 582 million messages (tweets) per day. To process the mass of Web 2.0's data (e.g. Twitter data) is a key functionality in modern IT landscapes of companies or institutions, because sentiments of users can be very valuable for the development of products, the enhancement of marketing strategies, or the prediction of political elections. This chapter's aim is to provide a framework for extracting, preprocessing, and analyzing customer sentiments in Twitter in all different areas.

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