Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis

Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, Roger Zimmermann


Abstract
Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.
Anthology ID:
N18-2043
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
266–270
Language:
URL:
https://aclanthology.org/N18-2043
DOI:
10.18653/v1/N18-2043
Bibkey:
Cite (ACL):
Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, and Roger Zimmermann. 2018. Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 266–270, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis (Hazarika et al., NAACL 2018)
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PDF:
https://preview.aclanthology.org/author-url/N18-2043.pdf