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Multimodal sentiment analysis is an active research area that combines multiple data modalities, e.g., text, image and audio, to analyze human emotions and benefits a variety of applications.
S. Asur and B. A. Huberman, “Predicting the future with social media,” in 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology , vol. 1, 2010, pp. 492–499
2010
Earlier work this paper cites.
J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” Journal of Computational Science , vol. 2, no. 1, pp. 1–8, 2011. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S187775031100007X
2011
Earlier work this paper cites.
A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe, “Election forecasts with twitter: How 140 characters reflect the political landscape,” Social Science Computer Review , vol. 29, no. 4, pp. 402–418, 2011
2011
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing , D. Yarowsky, T. Baldwin, A. Korhonen, K. Livescu, and S. Bethard, Eds. Seattle, Washington, USA: Association for Computational Linguistics, Oct. 2013, pp. 1631–1642. [Online]. Available: https://aclanthology.org/D13-1170/
2013
Earlier work this paper cites.
C. Gan, N. Wang, Y. Yang, D.-Y. Yeung, and A. G. Hauptmann, “Devnet: A deep event network for multimedia event detection and evidence recounting,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 2568–2577
2015
Earlier work this paper cites.
N. Majumder, S. Poria, A. Gelbukh, and E. Cambria, “Deep learning-based document modeling for personality detection from text,” IEEE Intelligent Systems , vol. 32, no. 2, pp. 74–79, 2017
2017
Earlier work this paper cites.
A. Zadeh, M. Chen, S. Poria, E. Cambria, and L. Morency, “Tensor fusion network for multimodal sentiment analysis,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017 . Association for Computational Linguistics, 2017, pp. 1103–1114. [Online]. Available: https://doi.org/10.18653/v1/d17-1115
2017
Earlier work this paper cites.
J. Williams, S. Kleinegesse, R. Comanescu, and O. Radu, “Recognizing emotions in video using multimodal DNN feature fusion,” in Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML) , A. Zadeh, P. P. Liang, L.-P. Morency, S. Poria, E. Cambria, and S. Scherer, Eds., Melbourne, Australia, Jul. 2018, pp. 11–19. [Online]. Available: https://aclanthology.org/W18-3302
2018
Earlier work this paper cites.
Z. Liu, Y. Shen, V. B. Lakshminarasimhan, P. P. Liang, A. Zadeh, and L. Morency, “Efficient low-rank multimodal fusion with modality-specific factors,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers . Association for Computational Linguistics, 2018, pp. 2247–2256. [Online]. Available: https://aclanthology.org/P18-1209/
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Zadeh, P. P. Liang, N. Mazumder, S. Poria, E. Cambria, and L. Morency, “Memory fusion network for multi-view sequential learning,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 . AAAI Press, 2018, pp. 5634–5641. [Online]. Available: https://doi.org/10.1609/aaai.v32i1.12021
2018
Earlier work this paper cites.
Y. Wang, Y. Shen, Z. Liu, P. P. Liang, A. Zadeh, and L. Morency, “Words can shift: Dynamically adjusting word representations using nonverbal behaviors,” in The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019 . AAAI Press, 2019, pp. 7216–7223. [Online]. Available: https://doi.org/10.1609/aaai.v33i01.33017216
2019
Earlier work this paper cites.
Y.-H. H. Tsai, S. Bai, P. P. Liang, J. Z. Kolter, L.-P. Morency, and R. Salakhutdinov, “Multimodal transformer for unaligned multimodal language sequences,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Florence, Italy: Association for Computational Linguistics, Jul. 2019, pp. 6558–6569
2019
Earlier work this paper cites.
Z. Sun, P. K. Sarma, W. A. Sethares, and Y. Liang, “Learning relationships between text, audio, and video via deep canonical correlation for multimodal language analysis,” in AAAI Conference on Artificial Intelligence , 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:207930647
2019
Earlier work this paper cites.
D. Hazarika, R. Zimmermann, and S. Poria, “MISA: modality-invariant and -specific representations for multimodal sentiment analysis,” in MM ’20: The 28th ACM International Conference on Multimedia, Virtual Event / Seattle, WA, USA, October 12-16, 2020 . ACM, 2020, pp. 1122–1131. [Online]. Available: https://doi.org/10.1145/3394171.3413678
2020
Earlier work this paper cites.
A. Kumar and J. Vepa, “Gated mechanism for attention based multi modal sentiment analysis,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020, pp. 4477–4481
2020
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 , ser. MM ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 521–528. [Online]. Available: https://doi.org/10.1145/3394171.3413690
2020
Cited alongside, same era.
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. 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 . Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 9180–9192
H. Wang, X. Li, Z. Ren, M. Wang, and C. Ma, “Multimodal sentiment analysis representations learning via contrastive learning with condense attention fusion,” Sensors , vol. 23, no. 5, 2023
2023
Later among the works it cites.
Y. Liu, Z. Li, K. Zhou, L. Zhang, L. Li, P. Tian, and S. Shen, “Scanning, attention, and reasoning multimodal content for sentiment analysis,” Knowledge-Based Systems , vol. 268, p. 110467, 2023
2023
Later among the works it cites.
X. Wang, M. Zhang, B. Chen, D. Wei, and Y. Shao, “Dynamic weighted multitask learning and contrastive learning for multimodal sentiment analysis,” Electronics , vol. 12, no. 13, 2023
2023
Later among the works it cites.
T. Yu, H. Gao, T.-E. Lin, M. Yang, Y. Wu, W. Ma, C. Wang, F. Huang, and Y. Li, “Speech-text pre-training for spoken dialog understanding with explicit cross-modal alignment,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 7900–7913
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2021
Cited alongside, same era.
Z. Khan and Y. Fu, “Exploiting BERT for multimodal target sentiment classification through input space translation,” in Proceedings of the 29th ACM International Conference on Multimedia . ACM, oct 2021. [Online]. Available: https://doi.org/10.1145%2F3474085.3475692
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 Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021 . AAAI Press, 2021, pp. 10 790–10 797. [Online]. Available: https://doi.org/10.1609/aaai.v35i12.17289
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,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 12, pp. 10 790–10 797, May 2021. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/17289
2021
Cited alongside, same era.
G. Paraskevopoulos, E. Georgiou, and A. Potamianos, “Mmlatch: Bottom-up top-down fusion for multimodal sentiment analysis,” in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2022, pp. 4573–4577
2022
Cited alongside, same era.
Y. Liu, S. Li, Y. Wu, C. W. Chen, Y. Shan, and X. Qie, “UMT: unified multi-modal transformers for joint video moment retrieval and highlight detection,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022 . IEEE, 2022, pp. 3032–3041. [Online]. Available: https://doi.org/10.1109/CVPR52688.2022.00305
2022
Cited alongside, same era.
Z. Quan, T. Sun, M. Su, J. Wei, X. Zhang, and S. Zhong, “Multimodal sentiment analysis based on nonverbal representation optimization network and contrastive interaction learning,” in 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , 2022, pp. 3086–3091
2022
Cited alongside, same era.
Y. He, L. Sun, Z. Lian, B. Liu, J. Tao, M. Wang, and Y. Cheng, “Multimodal temporal attention in sentiment analysis,” ser. MuSe’ 22. New York, NY, USA: Association for Computing Machinery, 2022, p. 61–66
2022
Cited alongside, same era.
J. He and H. Hu, “Mf-bert: Multimodal fusion in pre-trained bert for sentiment analysis,” IEEE Signal Processing Letters , vol. 29, pp. 454–458, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Later among the works it cites.
P. He, H. Qi, and S. Wang, “Cross-modal sentiment analysis of text and video based on bi-gru cyclic network and correlation enhancement,” Applied Sciences , vol. 13, no. 13, pp. 7489–7489, 2023
2023
Later among the works it cites.
H. Cheng, Z. Yang, X. Zhang, and Y. Yang, “Multimodal sentiment analysis based on attentional temporal convolutional network and multi-layer feature fusion,” IEEE Transactions on Affective Computing , vol. 14, no. 4, pp. 3149–3163, 2023
2023
Later among the works it cites.
R. Lin and H. Hu, “Multi-task momentum distillation for multimodal sentiment analysis,” IEEE Transactions on Affective Computing , pp. 1–18, 2023
2023
Later among the works it cites.
J. He, B. Su, Z. Sheng, C. Zhang, and H. Yang, “Adversarial invariant-specific representations fusion network for multimodal sentiment analysis,” in International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023) , vol. 12707, International Society for Optics and Photonics. SPIE, 2023, p. 127073R
2023
Later among the works it cites.
J. Yang, Y. Zhou, and H. Huang, “Mel-s3r: Combining mel-spectrogram and self-supervised speech representation with vq-vae for any-to-any voice conversion,” Speech Communication , vol. 151, pp. 52–63, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167639323000663
2023
Later among the works it cites.
C. Li, Z. Gan, Z. Yang, J. Yang, L. Li, L. Wang, and J. Gao, “Multimodal foundation models: From specialists to general-purpose assistants,” Found. Trends Comput. Graph. Vis. , vol. 16, pp. 1–214, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:262055614
2023
Later among the works it cites.
Z. Xie, Y. Yang, J. Wang, X. Liu, and X. Li, “Trustworthy multimodal fusion for sentiment analysis in ordinal sentiment space,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 8, pp. 7657–7670, 2024
2024
Closest in time.
K. Zhu, C. Fan, J. Tao, J. Xue, H. Xie, X. Liu, Y. Li, Z. Wen, and Z. Lv, “Dual-view multimodal interaction in multimodal sentiment analysis,” in 2024 IEEE International Conference on Multimedia and Expo (ICME) , 2024, pp. 1–6
2024
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M. Jin, L. Shao, X. Wang, Q. Yan, Z. Chu, T. Luo, J. Tang, and Q. Gao, “Aspect based sentiment analysis on multimodal data: A transformer and low-rank fusion approach,” in 2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI) , 2024, pp. 332–338
2024
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L. Sun, Z. Lian, B. Liu, and J. Tao, “Efficient multimodal transformer with dual-level feature restoration for robust multimodal sentiment analysis,” IEEE Transactions on Affective Computing , vol. 15, no. 1, pp. 309–325, 2024
2024
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L. Fang, G. Liu, and R. Zhang, “Multi-grained multimodal interaction network for sentiment analysis,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2024, pp. 7730–7734
2024
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C. Lin, H. Cheng, Q. Rao, and Y. Yang, “M 3
2024
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