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.
|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.|
|Number of pages||6|
|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
|Name||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Other||17th IEEE International Conference on Data Mining, ICDM 2017|
|Period||17/11/18 → 17/11/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1701-01.
© 2017 IEEE.
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