Title :
Examining the performance of topic modeling techniques in Twitter trends extraction
Author :
Kurniati, Mutia N. ; Woo-Jong Ryu ; Alam, Md Hasibul ; Sangkeun Lee
Author_Institution :
Dept. of Comput. Sci. & Eng., Korea Univ., Seoul, South Korea
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.
Keywords :
social networking (online); text analysis; JMTS; LDA; Twitter trends extraction; frequent phrase method; latent dirichlet allocation; semantic-based joint multigrain topic-sentiment; topic modeling techniques; Accuracy; Context; Games; Market research; Noise; Tablet computers; Twitter;
Conference_Titel :
Information Networking (ICOIN), 2014 International Conference on
Conference_Location :
Phuket
DOI :
10.1109/ICOIN.2014.6799706
