Examining the performance of topic modeling techniques in Twitter trends extraction

Mutia N. Kurniati; Woo-Jong Ryu; Md. Hijbul Alam; SangKeun Lee

doi:10.1109/ICOIN.2014.6799706

2014 International Conference on Information Networking (ICOIN)

Examining the performance of topic modeling techniques in Twitter trends extraction

Year: 2014, Pages: 364-369

DOI Bookmark: 10.1109/ICOIN.2014.6799706

Authors

Mutia N. Kurniati, Department of Computer Science and Engineering, Korea University, Seoul, South Korea 136-701
Woo-Jong Ryu, Department of Computer Science and Engineering, Korea University, Seoul, South Korea 136-701
Md. Hijbul Alam, Department of Computer Science and Engineering, Korea University, Seoul, South Korea 136-701
SangKeun Lee, Department of Computer Science and Engineering, Korea University, Seoul, South Korea 136-701

Abstract

It is very important to extract the Twitter trends since it reflects the personal view over 645 million of its users. We examine the effectiveness of two topic modeling techniques i.e., standard Latent Dirichlet Allocation (LDA) and semantic-based Joint Multi-grain Topic-Sentiment (JMTS) in Twitter trends extraction. In addition, we also examine the frequent phrase method. Our finding reveals that JMTS significantly outperforms frequent phrase method and LDA by 54% and 24%, respectively.

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Examining the performance of topic modeling techniques in Twitter trends extraction

Authors

Abstract

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