Aspect sentiment model for micro reviews

Reinald Kim Amplayo; Seung Won Hwang

doi:10.1109/ICDM.2017.83

Aspect sentiment model for micro reviews

Reinald Kim Amplayo, Seung Won Hwang

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 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 language	English
Title of host publication	Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
Editors	George Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	727-732
Number of pages	6
ISBN (Electronic)	9781538638347
DOIs	https://doi.org/10.1109/ICDM.2017.83
Publication status	Published - 2017 Dec 15
Event	17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States Duration: 2017 Nov 18 → 2017 Nov 21

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
Volume	2017-November
ISSN (Print)	1550-4786

Other

Other	17th IEEE International Conference on Data Mining, ICDM 2017
Country/Territory	United States
City	New Orleans
Period	17/11/18 → 17/11/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1701-01.

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Engineering(all)

Access to Document

10.1109/ICDM.2017.83

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).

@inproceedings{6fa8ba9a6e9f45949a3c6a1609edda53,

title = "Aspect sentiment model for micro reviews",

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.",

author = "Amplayo, {Reinald Kim} and Hwang, {Seung Won}",

note = "Funding Information: ACKNOWLEDGMENTS This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1701-01. Publisher Copyright: {\textcopyright} 2017 IEEE.; 17th IEEE International Conference on Data Mining, ICDM 2017 ; Conference date: 18-11-2017 Through 21-11-2017",

year = "2017",

month = dec,

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doi = "10.1109/ICDM.2017.83",

language = "English",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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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 proceeding › Conference contribution

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PY - 2017/12/15

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N2 - 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.

AB - 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.

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Y2 - 18 November 2017 through 21 November 2017

ER -

Aspect sentiment model for micro reviews

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