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

Kar Wai Lim; Wray Lindsay Buntine

doi:10.1145/2661829.2662005

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

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Original language	English
Title of host publication	Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14)
Editors	Minos Garofalakis, Ian Soboroff, Torsten Suel, Min Wang
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	1319 - 1328
Number of pages	10
ISBN (Print)	9781450325981
DOIs	https://doi.org/10.1145/2661829.2662005
Publication status	Published - 2014
Event	ACM International Conference on Information and Knowledge Management 2014 - Shanghai, China Duration: 3 Nov 2014 → 7 Nov 2014 Conference number: 23rd https://dl.acm.org/doi/proceedings/10.1145/2661829

Conference

Conference	ACM International Conference on Information and Knowledge Management 2014
Abbreviated title	CIKM 2014
Country/Territory	China
City	Shanghai
Period	3/11/14 → 7/11/14
Internet address	https://dl.acm.org/doi/proceedings/10.1145/2661829

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10.1145/2661829.2662005

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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). 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",

note = "ACM International Conference on Information and Knowledge Management 2014, CIKM 2014 ; Conference date: 03-11-2014 Through 07-11-2014",

url = "https://dl.acm.org/doi/proceedings/10.1145/2661829",

}

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 proceeding › Conference Paper › Research › peer-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

N1 - Conference code: 23rd

PY - 2014

Y1 - 2014

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

UR - https://www.scopus.com/pages/publications/84937622580

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

T2 - ACM International Conference on Information and Knowledge Management 2014

Y2 - 3 November 2014 through 7 November 2014

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 doi: 10.1145/2661829.2662005

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

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