Aspect sentiment model for micro reviews

Reinald Kim Amplayo, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews. This task is important in order to understand short reviews majority of the users write, while existing topic models are targeted for expert-level long reviews with sufficient co-occurrence patterns to observe. Current methods on aggregating micro reviews using metadata information may not be effective as well due to metadata absence, topical heterogeneity, and cold start problems. To this end, we propose a model called Micro Aspect Sentiment Model (MicroASM). MicroASM is based on the observation that short reviews 1) are viewed with sentiment-aspect word pairs as building blocks of information, and 2) can be clustered into larger reviews. When compared to the current state-of-the-art aspect sentiment models, experiments show that our model provides better performance on aspect-level tasks such as aspect term extraction and document-level tasks such as sentiment classification.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
EditorsGeorge Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages727-732
Number of pages6
ISBN (Electronic)9781538638347
DOIs
Publication statusPublished - 2017 Dec 15
Event17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States
Duration: 2017 Nov 182017 Nov 21

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2017-November
ISSN (Print)1550-4786

Other

Other17th IEEE International Conference on Data Mining, ICDM 2017
CountryUnited States
CityNew Orleans
Period17/11/1817/11/21

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Metadata
Experiments

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Amplayo, R. K., & Hwang, S. W. (2017). Aspect sentiment model for micro reviews. In G. Karypis, S. Alu, V. Raghavan, X. Wu, & L. Miele (Eds.), Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017 (pp. 727-732). (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2017-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2017.83
Amplayo, Reinald Kim ; Hwang, Seung Won. / Aspect sentiment model for micro reviews. Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. editor / George Karypis ; Srinivas Alu ; Vijay Raghavan ; Xindong Wu ; Lucio Miele. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 727-732 (Proceedings - IEEE International Conference on Data Mining, ICDM).
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Amplayo, RK & Hwang, SW 2017, Aspect sentiment model for micro reviews. in G Karypis, S Alu, V Raghavan, X Wu & L Miele (eds), Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 2017-November, Institute of Electrical and Electronics Engineers Inc., pp. 727-732, 17th IEEE International Conference on Data Mining, ICDM 2017, New Orleans, United States, 17/11/18. https://doi.org/10.1109/ICDM.2017.83

Aspect sentiment model for micro reviews. / Amplayo, Reinald Kim; Hwang, Seung Won.

Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. ed. / George Karypis; Srinivas Alu; Vijay Raghavan; Xindong Wu; Lucio Miele. Institute of Electrical and Electronics Engineers Inc., 2017. p. 727-732 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2017-November).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Amplayo RK, Hwang SW. Aspect sentiment model for micro reviews. In Karypis G, Alu S, Raghavan V, Wu X, Miele L, editors, Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 727-732. (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2017.83