Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks

Binxuan Huang, Kathleen Carley


Abstract
Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. Previous neural network based methods largely ignore the syntax structure in one sentence. In this paper, we propose a novel target-dependent graph attention network (TD-GAT) for aspect level sentiment classification, which explicitly utilizes the dependency relationship among words. Using the dependency graph, it propagates sentiment features directly from the syntactic context of an aspect target. In our experiments, we show our method outperforms multiple baselines with GloVe embeddings. We also demonstrate that using BERT representations further substantially boosts the performance.
Anthology ID:
D19-1549
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5469–5477
Language:
URL:
https://aclanthology.org/D19-1549
DOI:
10.18653/v1/D19-1549
Bibkey:
Cite (ACL):
Binxuan Huang and Kathleen Carley. 2019. Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5469–5477, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks (Huang & Carley, EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/D19-1549.pdf