Aspect sentiment model for micro reviews

Reinald Kim Amplayo, Seung Won Hwang

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

15 Citations (Scopus)


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.
Number of pages6
ISBN (Electronic)9781538638347
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
ISSN (Print)1550-4786


Other17th IEEE International Conference on Data Mining, ICDM 2017
Country/TerritoryUnited States
CityNew Orleans

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)


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