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Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

Chi Sun, Luyao Huang, Xipeng Qiu


Abstract
Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair.
Anthology ID:
N19-1035
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
380–385
Language:
URL:
https://aclanthology.org/N19-1035
DOI:
10.18653/v1/N19-1035
Bibkey:
Cite (ACL):
Chi Sun, Luyao Huang, and Xipeng Qiu. 2019. Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 380–385, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (Sun et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1035.pdf
Code
 HSLCY/ABSA-BERT-pair +  additional community code
Data
SemEval-2014 Task-4