• DocumentCode
    128472
  • 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
  • fYear
    2014
  • fDate
    10-12 Feb. 2014
  • Firstpage
    364
  • Lastpage
    369
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Networking (ICOIN), 2014 International Conference on
  • Conference_Location
    Phuket
  • Type

    conf

  • DOI
    10.1109/ICOIN.2014.6799706
  • Filename
    6799706
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=128472