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Emotion semantic inconsistency is an ubiquitous challenge in multi-modal sentiment analysis (MSA).
A. Zadeh et al. , “Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages,” IEEE Intelligent Systems , vol. 31, no. 6, pp. 82–88, 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Ghosal et al. , “Contextual inter-modal attention for multi-modal sentiment analysis,” in proceedings of the 2018 conference on empirical methods in natural language processing , 2018, pp. 3454–3466
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. B. Zadeh et al. , “Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp. 2236–2246
2018
Earlier work this paper cites.
J. Williams et al. , “Recognizing emotions in video using multimodal dnn feature fusion,” in Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML) . Association for Computational Linguistics, 2018, pp. 11–19
2018
Earlier work this paper cites.
E. Cambria et al. , “Benchmarking multimodal sentiment analysis,” in Computational Linguistics and Intelligent Text Processing: 18th International Conference, CICLing 2017, Budapest, Hungary, April 17–23, 2017, Revised Selected Papers, Part II 18 . Springer, 2018, pp. 166–179
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Zadeh et al. , “Memory fusion network for multi-view sequential learning,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
J. D. M.-W. C. Kenton and L. K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of naacL-HLT , vol. 1, 2019, p. 2
2019
Earlier work this paper cites.
Y.-H. H. Tsai et al. , “Multimodal transformer for unaligned multimodal language sequences,” in Proceedings of the conference. Association for Computational Linguistics. Meeting , vol. 2019. NIH Public Access, 2019, p. 6558
2019
Earlier work this paper cites.
K. Yang, H. Xu, and K. Gao, “Cm-bert: Cross-modal bert for text-audio sentiment analysis,” in Proceedings of the 28th ACM international conference on multimedia , 2020, pp. 521–528
2020
Earlier work this paper cites.
W. Yu, H. Xu, F. Meng, Y. Zhu, Y. Ma, J. Wu, J. Zou, and K. Yang, “CH-SIMS: A Chinese multimodal sentiment analysis dataset with fine-grained annotation of modality,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , D. Jurafsky, J. Chai, N. Schluter, and J. Tetreault, Eds. Online: Association for Computational Linguistics, Jul. 2020, pp. 3718–3727. [Online]. Available: https://aclanthology.org/2020.acl-main.343
2020
Cited alongside, same era.
W. Yu et al. , “Ch-sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality,” in Proceedings of the 58th annual meeting of the association for computational linguistics , 2020, pp. 3718–3727
2020
Cited alongside, same era.
2020
Cited alongside, same era.
P. Poklukar et al. , “Geometric multimodal contrastive representation learning,” in International Conference on Machine Learning . PMLR, 2022, pp. 17 782–17 800
2022
Later among the works it cites.
Y. Liu et al. , “Make acoustic and visual cues matter: Ch-sims v2. 0 dataset and av-mixup consistent module,” in Proceedings of the 2022 International Conference on Multimodal Interaction , 2022, pp. 247–258
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Sun et al. , “Layer-wise fusion with modality independence modeling for multi-modal emotion recognition,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2023, pp. 658–670
2023
Later among the works it cites.
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Y. Wu et al. , “A text-centered shared-private framework via cross-modal prediction for multimodal sentiment analysis,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , 2021, pp. 4730–4738
2021
Cited alongside, same era.
P. Huang et al. , “Text sentiment analysis based on bert and convolutional neural networks,” in Proceedings of the 2021 5th International Conference on Natural Language Processing and Information Retrieval , 2021, pp. 1–7
2021
Cited alongside, same era.
F. Lv et al. , “Progressive modality reinforcement for human multimodal emotion recognition from unaligned multimodal sequences,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2554–2562
2021
Cited alongside, same era.
W. Yu, H. Xu, Z. Yuan, and J. Wu, “Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis,” in Proceedings of the AAAI conference on artificial intelligence , vol. 35, no. 12, 2021, pp. 10 790–10 797
2021
Cited alongside, same era.
W. Han, H. Chen, and S. Poria, “Improving multimodal fusion with hierarchical mutual information maximization for multimodal sentiment analysis,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 9180–9192
2021
Cited alongside, same era.
R. Kaur and S. Kautish, “Multimodal sentiment analysis: A survey and comparison,” Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines , pp. 1846–1870, 2022
2022
Cited alongside, same era.
J. Li et al. , “Hybrid multimodal feature extraction, mining and fusion for sentiment analysis,” in Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge , 2022, pp. 81–88
2022
Cited alongside, same era.
R. Franceschini et al. , “Multimodal emotion recognition with modality-pairwise unsupervised contrastive loss,” in 2022 26th International Conference on Pattern Recognition (ICPR) . IEEE, 2022, pp. 2589–2596
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Zhu et al. , “Multimodal sentiment analysis based on fusion methods: A survey,” Information Fusion , vol. 95, pp. 306–325, 2023
2023
Later among the works it cites.
Z. Lian et al. , “Mer 2023: Multi-label learning, modality robustness, and semi-supervised learning,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 9610–9614
2023
Later among the works it cites.
D. Wang et al. , “Tetfn: A text enhanced transformer fusion network for multimodal sentiment analysis,” Pattern Recognition , vol. 136, p. 109259, 2023
2023
Later among the works it cites.