Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree

Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu

Abstract
We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence. Although a large majority of works typically focus on leveraging the expressive power of neural networks in handling this task, we explore the possibility of integrating dependency trees with neural networks for representation learning. To this end, we present a convolution over a dependency tree (CDT) model which exploits a Bi-directional Long Short Term Memory (Bi-LSTM) to learn representations for features of a sentence, and further enhance the embeddings with a graph convolutional network (GCN) which operates directly on the dependency tree of the sentence. Our approach propagates both contextual and dependency information from opinion words to aspect words, offering discriminative properties for supervision. Experimental results ranks our approach as the new state-of-the-art in aspect-based sentiment classification.
Anthology ID:
D19-1569
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
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5679–5688
Language:
URL:
https://aclanthology.org/D19-1569
DOI:
10.18653/v1/D19-1569
Bibkey:
Cite (ACL):
Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, and Xudong Liu. 2019. Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree. 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 5679–5688, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (Sun et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/ingestion-script-update/D19-1569.pdf
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