SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis

Bingbing Wang, Bin Liang, Jiachen Du, Min Yang, Ruifeng Xu


Abstract
This paper investigates the sentiment analysis task from a novel perspective by incorporating sentiment knowledge and eye movement into a graph architecture, aiming to draw the eye movement-based sentiment relationships for learning the sentiment expression of the context. To be specific, we first explore a linguistic probing eye movement paradigm to extract eye movement features based on the close relationship between linguistic features and the early and late processes of human reading behavior. Furthermore, to derive eye movement features with sentiment concepts, we devise a novel weighting strategy to integrate sentiment scores extracted from affective commonsense knowledge into eye movement features, called sentiment-eye movement weights. Then, the sentiment-eye movement weights are exploited to build the sentiment-eye movement guided graph (SEMGraph) model, so as to model the intricate sentiment relationships in the context. Experimental results on two sentiment analysis datasets with eye movement signals and three sentiment analysis datasets without eye movement signals show that the proposed SEMGraph achieves state-of-the-art performance, and can also be directly generalized to those sentiment analysis datasets without eye movement signals.
Anthology ID:
2022.emnlp-main.510
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7521–7531
Language:
URL:
https://aclanthology.org/2022.emnlp-main.510
DOI:
10.18653/v1/2022.emnlp-main.510
Bibkey:
Cite (ACL):
Bingbing Wang, Bin Liang, Jiachen Du, Min Yang, and Ruifeng Xu. 2022. SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7521–7531, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis (Wang et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.510.pdf