Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Original languageEnglish
    Title of host publicationProceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14)
    EditorsMinos Garofalakis, Ian Soboroff, Torsten Suel, Min Wang
    Place of PublicationNew York NY USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages1319 - 1328
    Number of pages10
    ISBN (Print)9781450325981
    DOIs
    Publication statusPublished - 2014
    EventACM International Conference on Information and Knowledge Management 2014 - Shanghai, China
    Duration: 3 Nov 20147 Nov 2014
    Conference number: 23rd

    Conference

    ConferenceACM International Conference on Information and Knowledge Management 2014
    Abbreviated titleCIKM 2014
    CountryChina
    CityShanghai
    Period3/11/147/11/14

    Cite this

    Lim, K. W., & Buntine, W. L. (2014). Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon. In M. Garofalakis, I. Soboroff, T. Suel, & M. Wang (Eds.), Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14) (pp. 1319 - 1328). New York NY USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/2661829.2662005
    Lim, Kar Wai ; Buntine, Wray Lindsay. / Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon. Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14). editor / Minos Garofalakis ; Ian Soboroff ; Torsten Suel ; Min Wang. New York NY USA : Association for Computing Machinery (ACM), 2014. pp. 1319 - 1328
    @inproceedings{b8756e522c654ee69cf3b787bb09670f,
    title = "Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon",
    author = "Lim, {Kar Wai} and Buntine, {Wray Lindsay}",
    year = "2014",
    doi = "10.1145/2661829.2662005",
    language = "English",
    isbn = "9781450325981",
    pages = "1319 -- 1328",
    editor = "Minos Garofalakis and Ian Soboroff and Torsten Suel and Min Wang",
    booktitle = "Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14)",
    publisher = "Association for Computing Machinery (ACM)",
    address = "United States of America",

    }

    Lim, KW & Buntine, WL 2014, Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon. in M Garofalakis, I Soboroff, T Suel & M Wang (eds), Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14). Association for Computing Machinery (ACM), New York NY USA, pp. 1319 - 1328, ACM International Conference on Information and Knowledge Management 2014, Shanghai, China, 3/11/14. https://doi.org/10.1145/2661829.2662005

    Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon. / Lim, Kar Wai; Buntine, Wray Lindsay.

    Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14). ed. / Minos Garofalakis; Ian Soboroff; Torsten Suel; Min Wang. New York NY USA : Association for Computing Machinery (ACM), 2014. p. 1319 - 1328.

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    TY - GEN

    T1 - Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon

    AU - Lim, Kar Wai

    AU - Buntine, Wray Lindsay

    PY - 2014

    Y1 - 2014

    UR - http://users.cecs.anu.edu.au/~karwailim/papers/cikm14/km0291-lim.pdf

    U2 - 10.1145/2661829.2662005

    DO - 10.1145/2661829.2662005

    M3 - Conference Paper

    SN - 9781450325981

    SP - 1319

    EP - 1328

    BT - Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14)

    A2 - Garofalakis, Minos

    A2 - Soboroff, Ian

    A2 - Suel, Torsten

    A2 - Wang, Min

    PB - Association for Computing Machinery (ACM)

    CY - New York NY USA

    ER -

    Lim KW, Buntine WL. Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon. In Garofalakis M, Soboroff I, Suel T, Wang M, editors, Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14). New York NY USA: Association for Computing Machinery (ACM). 2014. p. 1319 - 1328 https://doi.org/10.1145/2661829.2662005