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

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.