Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification

Fei Zhao, Zhen Wu, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen


Abstract
Target-oriented multimodal sentiment classification (TMSC) is a new subtask of aspect-based sentiment analysis, which aims to determine the sentiment polarity of the opinion target mentioned in a (sentence, image) pair. Recently, dominant works employ the attention mechanism to capture the corresponding visual representations of the opinion target, and then aggregate them as evidence to make sentiment predictions. However, they still suffer from two problems: (1) The granularity of the opinion target in two modalities is inconsistent, which causes visual attention sometimes fail to capture the corresponding visual representations of the target; (2) Even though it is captured, there are still significant differences between the visual representations expressing the same mood, which brings great difficulty to sentiment prediction. To this end, we propose a novel Knowledge-enhanced Framework (KEF) in this paper, which can successfully exploit adjective-noun pairs extracted from the image to improve the visual attention capability and sentiment prediction capability of the TMSC task. Extensive experimental results show that our framework consistently outperforms state-of-the-art works on two public datasets.
Anthology ID:
2022.coling-1.590
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6784–6794
Language:
URL:
https://aclanthology.org/2022.coling-1.590
DOI:
Bibkey:
Cite (ACL):
Fei Zhao, Zhen Wu, Siyu Long, Xinyu Dai, Shujian Huang, and Jiajun Chen. 2022. Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6784–6794, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (Zhao et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.590.pdf
Code
 1429904852/kef
Data
SemEval 2014 Task 4 Sub Task 2