Empirical study of topic modeling in Twitter
Published 25 July 2010
Liangjie Hong, Brian D. Davison
Citations1,138
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TL;DR
It is shown that by training a topic model on aggregated messages the authors can obtain a higher quality of learned model which results in significantly better performance in two real-world classification problems.
Abstract
Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of information for a wide spectrum of users. In Twitter, popular information that is deemed important by the community propagates through the network. Studying the characteristics of content in the messages becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, sentiment analysis and others. While many researchers wish to use standard text mining tools to understand messages on Twitter, the restricted length of those messages prevents them from being employed to their full potential.
Keywords
Computer SciencePhysics and Astronomy
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