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 (Telekom Deutschland GmbH, Germany), Tom Miller (T-Systems Multimedia Solutions GmbH, Germany) and Andreas Hilbert (Technische Universität Dresden, Germany)
Copyright: © 2016 |Pages: 17
DOI: 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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In the last five years many articles have been published in the area of Sentiment Analysis. Liu (2007) gives a broad overview of characteristics, tasks and methods of Sentiment Analysis and places them into the context of Web Data Mining. Next to the theoretical descriptions of Liu (2007) you can find various articles covering different existing Sentiment Analysis systems, which are systematically presented by Lee et al. (2008), as Table 1 shows.

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