Co-attention Guided Local-Global Feature Fusion for Aspect-Level Multimodal Sentiment Analysis
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8429-9_30
Reference31 articles.
1. Truong, Q.T., Lauw, H.W.: VistaNet: visual aspect attention network for multimodal sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 305–312 (2019)
2. Xu, N., Mao, W., Chen, G.: Multi-interactive memory network for aspect based multimodal sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 371–378 (2019)
3. Yu, J., Jiang, J., Xia, R.: Entity-sensitive attention and fusion network for entity-level multimodal sentiment classification. IEEE/ACM Trans. Audio Speech Lang. Process. 28, 429–439 (2019)
4. Gu, D., Wang, J., Cai, S.: Targeted aspect-based multimodal sentiment analysis: an attention capsule extraction and multi-head fusion network. IEEE Access 9, 157329–157336 (2021)
5. Xu, N., Mao, W., Chen, G.: A co-memory network for multimodal sentiment analysis. In: the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 929–932 (2018)
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