SWACL: Sentimental Words Aware Contrastive Learning for Multimodal Sentiment Analysis

Author:

Zhuang Junbin1ORCID,He Junyi1ORCID,Li Xia1ORCID

Affiliation:

1. School of Information Science and Technology, Guangdong University of Foreign Studies, China

Publisher

ACM

Reference34 articles.

1. Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency. 2018. Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence 41, 2 (2018), 423–443.

2. Adrien Bardes, Jean Ponce, and Yann LeCun. 2022. Variance-invariance-covariance regularization for self-supervised learning. ICLR, Vicreg (2022).

3. Erik Cambria, Qian Liu, Sergio Decherchi, Frank Xing, and Kenneth Kwok. 2022. SenticNet 7: A commonsense-based neurosymbolic AI framework for explainable sentiment analysis. In Proceedings of the Thirteenth Language Resources and Evaluation Conference. 3829–3839.

4. SWAFN: Sentimental Words Aware Fusion Network for Multimodal Sentiment Analysis

5. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597–1607.

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