Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings

Junjie Li, Haitong Yang, Chengqing Zong


Abstract
Document-level multi-aspect sentiment classification aims to predict user’s sentiment polarities for different aspects of a product in a review. Existing approaches mainly focus on text information. However, the authors (i.e. users) and overall ratings of reviews are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects in this paper. Therefore, we propose a model called Hierarchical User Aspect Rating Network (HUARN) to consider user preference and overall ratings jointly. Specifically, HUARN adopts a hierarchical architecture to encode word, sentence, and document level information. Then, user attention and aspect attention are introduced into building sentence and document level representation. The document representation is combined with user and overall rating information to predict aspect ratings of a review. Diverse aspects are treated differently and a multi-task framework is adopted. Empirical results on two real-world datasets show that HUARN achieves state-of-the-art performances.
Anthology ID:
C18-1079
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
925–936
Language:
URL:
https://aclanthology.org/C18-1079
DOI:
Bibkey:
Cite (ACL):
Junjie Li, Haitong Yang, and Chengqing Zong. 2018. Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings. In Proceedings of the 27th International Conference on Computational Linguistics, pages 925–936, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings (Li et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1079.pdf
Code
 Junjieli0704/HUARN