Quantum-inspired multimodal fusion for video sentiment analysis

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687 · see citing works

The mythos of model interpretability

Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works

The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.

Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427

Quantum Computation and Quantum Information: 10th Anniversary Edition

Nielsen, M. A., & Chuang, I. L. (2011) · Cited by 2,724 · see citing works

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

(2019) · doi:10.18653/v1/p19-1 · Cited by 2,426 · see citing works

Multimodal Transformer for Unaligned Multimodal Language Sequences

Tsai, Y.-H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L.-P., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1656 · Cited by 2,259 · cited 10× · see citing works

Tensor Fusion Network for Multimodal Sentiment Analysis

Zadeh, A., Chen, M., Poria, S., Cambria, E., & Morency, L.-P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1115 · Cited by 2,102 · see citing works

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

(2018) · doi:10.18653/v1/d18-1 · Cited by 1,688 · see citing works

Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph

Bagher Zadeh, A., Liang, P. P., Poria, S., Cambria, E., & Morency, L.-P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1208 · Cited by 1,433 · see citing works

Efficient Low-rank Multimodal Fusion With Modality-Specific Factors

Liu, Z., Shen, Y., Lakshminarasimhan, V. B., Liang, P. P., Bagher Zadeh, A., & Morency, L.-P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1209 · Cited by 1,170 · see citing works

Quantum Models of Cognition and Decision

Busemeyer, J., & Bruza, P. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9780511997716 · Cited by 1,066 · see citing works

Measures on the Closed Subspaces of a Hilbert Space

Gleason, A. M. (1975) · Springer eBooks · doi:10.1007/978-94-010-1795-4_7 · Cited by 1,011 · see citing works

Memory Fusion Network for Multi-view Sequential Learning

Zadeh, A., Liang, P. P., Mazumder, N., Poria, S., Cambria, E., & Morency, L.-P. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12021 · Cited by 855 · see citing works

Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06

Alfredo Cuzzocrea, J. A., Cuzzocrea, A., Allan, J., Paton, N. W., Srivastava, D., Agrawal, R., Broder, A., Zaki, M. J., Selçuk Candan, K., Labrinidis, A., et al. (2006) · doi:10.1145/1183614 · Cited by 756 · see citing works

Integrating Multimodal Information in Large Pretrained Transformers

Rahman, W., Hasan, M. K., Lee, S., Zadeh, A. B., Mao, C., Morency, L., & Hoque, M. E. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.214 · Cited by 743 · see citing works

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

(2016) · doi:10.18653/v1/n16-1 · Cited by 724 · see citing works

Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Vanderwende, L., Daumé, H., & Kirchhoff, K. (2013) · North American Chapter of the Association for Computational Linguistics · Cited by 685 · see citing works

Mathematical Foundations for a Compositional Distributional Model of MeaningPreprint

Coecke, B., Sadrzadeh, M., & Clark, S. (2010) · arXiv.org · doi:10.48550/arxiv.1003.4394 · Cited by 685 · see citing works

Measures on the Closed Subspaces of a Hilbert Space

Gleason, A. M. (1957) · Indiana University Mathematics Journal · doi:10.1512/iumj.1957.6.56050 · Cited by 666 · see citing works

Speaker identification on the SCOTUS corpus

Yuan, J., & Liberman, M. (2008) · The Journal of the Acoustical Society of America · doi:10.1121/1.2935783 · Cited by 600 · see citing works

Quantum Models of Cognition and Decision

Busemeyer, J. R., Wang, Z., & Pothos, E. (2015) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199957996.013.17 · Cited by 550 · see citing works

Multi-attention Recurrent Network for Human Communication Comprehension

Zadeh, A., Liang, P. P., Poria, S., Vij, P., Cambria, E., & Morency, L. (2018) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v32i1.12024 · Cited by 535 · see citing works

Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities

Pham, H., Liang, P. P., Manzini, T., Morency, L.-P., & Póczos, B. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016892 · Cited by 512 · see citing works

Axiom: A Householder-Parameterized Pure Unitary RNN for Long-Range Sequence ModelingPreprint

Arjovsky, M., Shah, A., & Bengio, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06464 · Cited by 510 · see citing works

Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors

Wang, Y., Shen, Y., Liu, Z., Liang, P. P., Zadeh, A., & Morency, L. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017216 · Cited by 493 · see citing works

The Geometry of Information Retrieval

van Rijsbergen, C. J. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511543333 · Cited by 395 · see citing works

The geometry of information retrieval

(2006) · Choice Reviews Online · doi:10.5860/choice.43-2848 · Cited by 390 · see citing works

MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos

Zadeh, A., Zellers, R., Pincus, E., & Morency, L.-P. (2016) · arXiv · doi:10.48550/arxiv.1606.06259 · Cited by 350 · see citing works

Learning Factorized Multimodal Representations

Tsai, Y.-H. H., Liang, P. P., Zadeh, A., Morency, L.-P., & Salakhutdinov, R. (2018) · arXiv · doi:10.48550/arxiv.1806.06176 · Cited by 323 · see citing works

Axiom: A Householder-Parameterized Pure Unitary RNN for Long-Range Sequence ModelingPreprint

Chaudhary, S. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20108465 · Cited by 321 · see citing works

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Barzilay, R., & Kan, M.-Y. (2017) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p17-1 · Cited by 269 · see citing works

Multimodal Saliency and Fusion for Movie Summarization Based on Aural, Visual, and Textual Attention

Evangelopoulos, G., Zlatintsi, A., Potamianos, A., Maragos, P. A., Rapantzikos, K. E., Σκούμας, Γ., & Avrithis, Y. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2267205 · Cited by 263 · see citing works

Full-Capacity Unitary Recurrent Neural NetworksPreprint

Wisdom, S., Powers, T., Hershey, J. R., Roux, J. L., & Atlas, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.00035 · Cited by 242 · see citing works

Multimodal Language Analysis with Recurrent Multistage Fusion

Liang, P. P., Liu, Z., Bagher Zadeh, A., & Morency, L.-P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1014 · Cited by 230 · see citing works

Black Holes and White Rabbits: Metaphor Identification with Visual Features

Shutova, E., Kiela, D., & Maillard, J. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1020 · Cited by 229 · see citing works

Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI

Holzinger, A., Kieseberg, P., Weippl, E. R., & Tjoa, A. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99740-7_1 · Cited by 210 · see citing works

Fuzzy commonsense reasoning for multimodal sentiment analysis

Chaturvedi, I., Satapathy, R., Cavallari, S., & Cambria, E. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.04.024 · Cited by 171 · see citing works

Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing

Mai, S., Hu, H., & Xing, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1046 · Cited by 153 · see citing works

Multimodal Machine Learning: A Survey and TaxonomyPreprint

Baltrušaitis, T., Ahuja, C., & Morency, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.09406 · Cited by 152 · see citing works

Multiple Classifier Systems for the Classification of Audio-Visual Emotional States

Glodek, M., Tschechne, S., Layher, G., Schels, M., Brosch, T., Scherer, S. J., Kächele, M., Schmidt, M., Heiko Neumann, B., Palm, G., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_47 · Cited by 133 · see citing works

Modeling term dependencies with quantum language models for IR

Sordoni, A., Nie, J., & Bengio, Y. (2013) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2484028.2484098 · Cited by 125 · see citing works

Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics

Cambria, E., Howard, N., Hsu, J. Y.-J., & Hussain, A. (2013) · IEEE Symposium on Computational Intelligence for Human-like Intelligence (CIHLI) · doi:10.1109/cihli.2013.6613272 · Cited by 123 · see citing works

Affective Computing and Intelligent Interaction

D’Mello, S., Martin, J.-C., Schuller, B., & Graesser, A. C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8 · Cited by 122 · see citing works

A quantum-inspired multimodal sentiment analysis framework

Zhang, Y., Song, D., Zhang, P., Wang, P., Li, J., Li, X., & Wang, B. (2018) · Theoretical Computer Science · doi:10.1016/j.tcs.2018.04.029 · Cited by 107 · see citing works

Introduction to Information Retrieval and Quantum Mechanics

Melucci, M. (2015) · ˜The œKluwer international series on information retrieval · doi:10.1007/978-3-662-48313-8 · Cited by 96 · see citing works

Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization

Liang, P. P., Liu, Z., Tsai, Y.-H. H., Zhao, Q., Salakhutdinov, R., & Morency, L.-P. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1152 · Cited by 95 · see citing works

Using the Quantum Probability Ranking Principle to Rank Interdependent Documents

Zuccon, G., & Azzopardi, L. A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12275-0_32 · Cited by 93 · see citing works

Efficient Low-rank Multimodal Fusion with Modality-Specific FactorsPreprint

Liu, Z., Shen, Y., Lakshminarasimhan, V., Liang, P. P., Zadeh, A., & Morency, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00064 · Cited by 80 · see citing works

CNM: An Interpretable Complex-valued Network for Matching

Li, Q., Wang, B., & Melucci, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1420 · Cited by 56 · see citing works

Machine Learning and Knowledge Extraction

Holzinger, A., Kieseberg, P., Tjoa, A. M., & Weippl, E. R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99740-7 · Cited by 46 · see citing works

Semantic Hilbert Space for Text Representation Learning

Wang, B., Li, Q., Melucci, M., & Song, D. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313516 · Cited by 45 · see citing works

A Quantum Many-body Wave Function Inspired Language Modeling Approach

Zhang, P., Su, Z., Zhang, L., Wang, B., & Song, D. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3271723 · Cited by 44 · see citing works

Exploration of Quantum Interference in Document Relevance Judgement Discrepancy

Wang, B., Zhang, P., Li, J., Song, D., Hou, Y., & Shang, Z. (2016) · Entropy · doi:10.3390/e18040144 · Cited by 43 · see citing works

Quantum-Inspired Complex Word Embedding

Li, Q., Uprety, S., Wang, B., & Song, D. (2018) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w18-3006 · Cited by 36 · see citing works

Modality-based Factorization for Multimodal Fusion

Barezi, E. J., & Fung, P. (2019) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · doi:10.18653/v1/w19-4331 · Cited by 35 · see citing works

A Quantum-Theoretic Approach to Distributional Semantics

Blacoe, W., Kashefi, E., & Lapata, M. (2013) · Cited by 32 · see citing works

Modeling Multi-query Retrieval Tasks Using Density Matrix Transformation

Li, Q., Li, J., Zhang, P., & Song, D. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767819 · Cited by 32 · see citing works

Modeling quantum entanglements in quantum language models

Xie, M., Hou, Y., Zhang, P., Li, J., Li, W., & Song, D. (2015) · Open Research Online (The Open University) · Cited by 28 · see citing works

Distributional Sentence Entailment Using Density Matrices

Balkır, E., Sadrzadeh, M., & Coecke, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-28678-5_1 · Cited by 26 · see citing works

Modeling latent topic interactions using quantum interference for information retrieval

Sordoni, A., He, J., & Nie, J. (2013) · Proceedings of the ... ACM International Conference on Information & Knowledge Management · doi:10.1145/2505515.2507854 · Cited by 24 · see citing works

Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

Augenstein, I., Gella, S., Ruder, S., Kann, K., Can, B., Conneau, A., Welbl, J., Ren, X., & Rei, M. (2019) · doi:10.18653/v1/w19-43 · Cited by 24 · see citing works

A Quantum Query Expansion Approach for Session Search

Zhang, P., Li, J., Wang, B., Zhao, X., Song, D., Hou, Y., & Melucci, M. (2016) · Entropy · doi:10.3390/e18040146 · Cited by 20 · see citing works

Proceedings of The Third Workshop on Representation Learning for NLP

(2018) · doi:10.18653/v1/w18-30 · Cited by 18 · see citing works

Investigating Non-classical Correlations Between Decision Fused Multi-modal Documents

Gkoumas, D., Uprety, S., & Song, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35895-2_11 · Cited by 10 · see citing works

2014 IEEE Symposium on Computational Intelligence for Human-like Intelligence (CIHLI)

(2014) · doi:10.1109/cihli33835.2014 · Cited by 9 · see citing works

Quantum-inspired Complex Word Embedding

Li, Q., Uprety, S., Wang, B., et al. (2018) · Open Research Online (The Open University) · doi:10.48550/arxiv.1805.11351 · Cited by 6 · see citing works

Topics in Theoretical Computer Science

Mousavi, M. R., & Sgall, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68953-1 · Cited by 4 · see citing works

Majority Vote of Diverse Classifiers for Late FusionPreprint

Morvant, E., Habrard, A., & Ayache, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.7796 · Cited by 1 · see citing works

Towards modeling implicit feedback with quantum entanglement

Melucci, M. (2008) · Padua Research Archive (University of Padova) · Cited by 1 · see citing works

Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions

Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.025 · Cited by 783 · see citing works

Multimodal sentiment analysis based on fusion methods: A survey

Zhu, L., Zhu, Z., Zhang, C., Xu, Y., & Kong, X. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.028 · Cited by 368 · see citing works

Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis

Mai, S., Zeng, Y., Zheng, S., & Hu, H. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3172360 · Cited by 301 · see citing works

Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations

Mai, S., Zeng, Y., & Hu, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3171679 · Cited by 182 · see citing works

Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a Survey

Abdu, S. A., Yousef, A. H., & Salem, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.003 · Cited by 147 · see citing works

Video sentiment analysis with bimodal information-augmented multi-head attention

Wu, T., Peng, J., Zhang, W., Zhang, H., Tan, S., Yi, F., Ma, C., & Huang, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107676 · Cited by 137 · see citing works

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

Faragallah, O. S., El‐Hoseny, H. M., El‐Shafai, W., El‐Rahman, W. A., El‐sayed, H. S., El‐Rabaie, E. M., El‐Samie, F. E. A., & Geweid, G. G. N. (2020) · IEEE Access · doi:10.1109/access.2020.3048315 · Cited by 118 · see citing works

Improving aspect-based sentiment analysis with Knowledge-aware Dependency Graph Network

Wu, H., Huang, C., & Deng, S. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.12.004 · Cited by 95 · see citing works

SMIN: Semi-Supervised Multi-Modal Interaction Network for Conversational Emotion Recognition

Lian, Z., Liu, B., & Tao, J. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3141237 · Cited by 89 · see citing works

Progress, achievements, and challenges in multimodal sentiment analysis using deep learning: A survey

Pandey, A., & Vishwakarma, D. K. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111206 · Cited by 84 · see citing works

Disentanglement Translation Network for multimodal sentiment analysis

Zeng, Y., Yan, W., Mai, S., & Hu, H. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102031 · Cited by 84 · see citing works

Heterogeneous graph convolution based on In-domain Self-supervision for Multimodal Sentiment Analysis

Zeng, Y., Li, Z., Tang, Z., Chen, Z., & Ma, H. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119240 · Cited by 74 · see citing works

Multi-Task Momentum Distillation for Multimodal Sentiment Analysis

Lin, R., & Hu, H. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3282410 · Cited by 71 · see citing works

Multimodal sentiment analysis based on multi-layer feature fusion and multi-task learning

Cai, Y., Li, X., Zhang, Y., Li, J., Zhu, F., & Rao, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-85859-6 · Cited by 68 · see citing works

Quantum Machine Learning: Scope for real-world problems

Jadhav, A., Rasool, A., & Gyanchandani, M. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.235 · Cited by 70 · see citing works

Learning to Balance the Learning Rates Between Various Modalities via Adaptive Tracking Factor

Ya, S., Mai, S., & Hu, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3101421 · Cited by 65 · see citing works

Survey on multimodal approaches to emotion recognition

Aruna Gladys, A., & Vetriselvi, V. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126693 · Cited by 62 · see citing works

A survey on sentiment analysis and its applications

Al-Qablan, T. A., Noor, M. H. M., Al‐Betar, M. A., & Khader, A. T. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08941-y · Cited by 69 · see citing works

Learning to Learn Better Unimodal Representations via Adaptive Multimodal Meta-Learning

Ya, S., Mai, S., & Hu, H. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3178231 · Cited by 59 · see citing works

The Weighted Cross-Modal Attention Mechanism With Sentiment Prediction Auxiliary Task for Multimodal Sentiment Analysis

Chen, Q., Huang, G., & Wang, Y. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3192728 · Cited by 58 · see citing works

TEDT: Transformer-Based Encoding–Decoding Translation Network for Multimodal Sentiment Analysis

Wang, F., Tian, S., Yu, L., Liu, J., Wang, J., Li, K., & Wang, Y. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10073-9 · Cited by 60 · see citing works

Excavating multimodal correlation for representation learning

Mai, S., Ya, S., Zeng, Y., & Hu, H. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.003 · Cited by 57 · see citing works

Dynamically Shifting Multimodal Representations via Hybrid-Modal Attention for Multimodal Sentiment Analysis

Lin, R., & Hu, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3303711 · Cited by 56 · see citing works

Joint multimodal sentiment analysis based on information relevance

Chen, D., Su, W., Wu, P., & Hua, B. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103193 · Cited by 56 · see citing works

Deep CNN with late fusion for real time multimodal emotion recognition

Dixit, C., & Satapathy, S. M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122579 · Cited by 58 · see citing works

Contemporary Quantum Computing Use Cases: Taxonomy, Review and Challenges

Singh, J., & Bhangu, K. S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09809-5 · Cited by 53 · see citing works

MF-BERT: Multimodal Fusion in Pre-Trained BERT for Sentiment Analysis

He, J., & Hu, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3139856 · Cited by 47 · see citing works

MIA-Net: Multi-Modal Interactive Attention Network for Multi-Modal Affective Analysis

Li, S., Zhang, T., Chen, B., & Chen, C. L. P. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3259010 · Cited by 47 · see citing works

Hybrid cross-modal interaction learning for multimodal sentiment analysis

Fu, Y., Zhang, Z., Yang, R., & Yao, C. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127201 · Cited by 46 · see citing works

AIA-Net: Adaptive Interactive Attention Network for Text–Audio Emotion Recognition

Zhang, T., Li, S., Chen, B., Yuan, H., & Chen, C. L. P. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3195739 · Cited by 44 · see citing works

A feature-based restoration dynamic interaction network for multimodal sentiment analysis

Zeng, Y., Li, Z., Chen, Z., & Ma, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107335 · Cited by 42 · see citing works

Multimodal negative sentiment recognition of online public opinion on public health emergencies based on graph convolutional networks and ensemble learning

Zeng, Z., Sun, S., & Li, Q. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103378 · Cited by 39 · see citing works

Breaking down linguistic complexities: A structured approach to aspect-based sentiment analysis

Ahmed, K., Nadeem, M. I., Zheng, Z.-Y., Li, D., Ullah, I., Assam, M., Ghadi, Y., & Mohamed, H. G. (2023) · Journal of King Saud University: Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101651 · Cited by 43 · see citing works

Learning from the global view: Supervised contrastive learning of multimodal representation

Mai, S., Zeng, Y., & Hu, H. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101920 · Cited by 41 · see citing works

Sentiment analysis from Customer-generated online videos on product review using topic modeling and Multi-attention BLSTM

Wang, Z., Gao, P., & Chu, X. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101588 · Cited by 40 · see citing works

What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability

Liu, Y., Zhang, Y., Li, Q., Wang, B., & Song, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.74 · Cited by 37 · see citing works

Adapt and explore: Multimodal mixup for representation learning

Lin, R., & Hu, H. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102216 · Cited by 34 · see citing works

Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis

Gkoumas, D., Li, Q., Dehdashti, S., Melucci, M., Yu, Y., & Song, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16165 · Cited by 34 · see citing works

A Survey of Quantum-cognitively Inspired Sentiment Analysis Models

Liu, Y., Li, Q., Wang, B., Zhang, Y., & Song, D. (2023) · ACM Computing Surveys · doi:10.1145/3604550 · Cited by 39 · see citing works

Multimodal Graph for Unaligned Multimodal Sequence Analysis via Graph Convolution and Graph Pooling

Mai, S., Xing, S., He, J., Zeng, Y., & Hu, H. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3542927 · Cited by 61 · see citing works

Multimodal knowledge graph construction of Chinese traditional operas and sentiment and genre recognition

Fan, T., Wang, H., & Hodel, T. (2023) · Journal of Cultural Heritage · doi:10.1016/j.culher.2023.05.003 · Cited by 36 · see citing works

Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media

Olaniyan, D., Ogundokun, R. O., Bernard, O. P., Olaniyan, J., Maskeliūnas, R., & Akande, H. B. (2023) · Computers · doi:10.3390/computers12110231 · Cited by 31 · see citing works

Multimodal Sentiment Recognition With Multi-Task Learning

Zhang, S., Yin, C., & Yin, Z. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3224929 · Cited by 31 · see citing works

Information fusion for affective computing and sentiment analysis

Hussain, A., Cambria, E., Poria, S., Hawalah, A. Y. A., & Herrera, F. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.010 · Cited by 30 · see citing works

MLG-NCS: Multimodal Local–Global Neuromorphic Computing System for Affective Video Content Analysis

Ji, X., Dong, Z., Zhou, G., Lai, C. S., & Qi, D. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3392732 · Cited by 30 · see citing works

PIRNet: Personality-Enhanced Iterative Refinement Network for Emotion Recognition in Conversation

Lian, Z., Liu, B., & Tao, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3192469 · Cited by 32 · see citing works

Multimodal Boosting: Addressing Noisy Modalities and Identifying Modality Contribution

Mai, S., Ya, S., Xiong, A., Zeng, Y., & Hu, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3306489 · Cited by 29 · see citing works

Learning with density matrices and random features

Gonzalez, F. A., Gallego-Mejia, J. A., Toledo-Cortés, S., & Vargas-Calderón, V. (2022) · Quantum Machine Intelligence · doi:10.1007/s42484-022-00079-9 · Cited by 27 · see citing works

Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis

Zeng, Y., Mai, S., & Hu, H. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.109 · Cited by 31 · see citing works

Multimodal sentiment analysis based on disentangled representation learning and cross-modal-context association mining

Li, Z., Liu, P., Pan, Y., Ding, W., Yu, J., Chen, H., Liu, W., Luo, Y., & Wang, H. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128940 · Cited by 25 · see citing works

Dynamic Invariant-Specific Representation Fusion Network for Multimodal Sentiment Analysis

He, J., Yanga, H., Zhang, C., Chen, H., & Xua, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2105593 · Cited by 25 · see citing works

Multimodal Reaction: Information Modulation for Cross-Modal Representation Learning

Zeng, Y., Mai, S., Yan, W., & Hu, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3293335 · Cited by 24 · see citing works

D-NISQ: A reference model for Distributed Noisy Intermediate-Scale Quantum computers

Acampora, G., Martino, F. D., Massa, A. A., Schiattarella, R., & Vitiello, A. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.08.003 · Cited by 23 · see citing works

Intangible cultural heritage image classification with multimodal attention and hierarchical fusion

Fan, T., Wang, H., & Deng, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120555 · Cited by 22 · see citing works

Deep Tensor Evidence Fusion Network for Sentiment Classification

Wang, Z., Xu, G., Zhou, X., Kim, J., Zhu, H., & Deng, L. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3197994 · Cited by 21 · see citing works

Emotion Quantification Using Variational Quantum State Fidelity Estimation

Singh, J., Ali, F., Shah, B., Bhangu, K. S., & Kwak, D. (2022) · IEEE Access · doi:10.1109/access.2022.3216890 · Cited by 18 · see citing works

Moving From Narrative to Interactive Multi-Modal Sentiment Analysis: A Survey

Ma, J., Rong, L., Zhang, Y., & Tiwari, P. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3610288 · Cited by 20 · see citing works

A Multi-Modal ELMo Model for Image Sentiment Recognition of Consumer Data

Rong, L., Ding, Y., Wang, M., Saddik, A. E., & Hossain, M. S. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3357543 · Cited by 18 · see citing works

Sentiment analysis on a low-resource language dataset using multimodal representation learning and cross-lingual transfer learning

Aruna Gladys, A., & Vetriselvi, V. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111553 · Cited by 23 · see citing works

Multimodal sentiment analysis method based on image-text quantum transformer

Wu, H., Kong, D., Wang, L., Li, D., Zhang, J., & Han, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130107 · Cited by 17 · see citing works

Cross-modal context-gated convolution for multi-modal sentiment analysis

Wen, H., You, S., & Fu, Y. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.025 · Cited by 18 · see citing works

Expandable quantum deep width learning-based distributed voltage control for smart grids with high penetration of distributed energy resources

Yin, L., & Lu, Y. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107861 · Cited by 16 · see citing works

Learning inter-class optical flow difference using generative adversarial networks for facial expression recognition

Guo, W., Zhao, X., Zhang, S., & Pan, X. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13360-7 · Cited by 16 · see citing works

An Entanglement-driven Fusion Neural Network for Video Sentiment Analysis

Gkoumas, D., Li, Q., Yu, Y., & Song, D. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/239 · Cited by 15 · see citing works

Complex-valued Neural Network-based Quantum Language Models

Zhang, P., Hui, W., Wang, B., Zhao, D., Song, D., Lioma, C., & Simonsen, J. G. (2022) · ACM Transactions on Information Systems · doi:10.1145/3505138 · Cited by 16 · see citing works

Quantum-Enhanced Support Vector Classifier for Image Classification

Golchha, R., & Verma, G. K. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126421 · Cited by 14 · see citing works

Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision

Zheng, W., Yan, L., Gou, C., & Wang, F. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10166-9 · Cited by 15 · see citing works

A survey on quantum deep learning

Wu, H., Zhang, J., Wang, L., Li, D., Kong, D., & Han, Y. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07083-3 · Cited by 14 · see citing works

Reconstructing representations using diffusion models for multimodal sentiment analysis through reading comprehension

Zhang, H., Yan, Y., Cai, Z., Zhan, P., Chen, B. Y., Jiang, B., & Xie, B. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112346 · Cited by 13 · see citing works

Curriculum Learning Meets Weakly Supervised Multimodal Correlation Learning

Mai, S., Ya, S., & Hu, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.209 · Cited by 13 · see citing works

Global distilling framework with cognitive gravitation for multimodal emotion recognition

Li, H., Zhong, H., Xu, C., Liu, X., Wen, G., & Liu, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2024.129306 · Cited by 9 · see citing works

Multilevel information compression and textual information enhancement for multimodal sentiment analysis

Zhang, Y., Zhong, H., Alhusaini, N., Chen, G., & Wu, C. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113121 · Cited by 9 · see citing works

Multimodal Emotion Recognition via the Fusion of Mamba and Liquid Neural Networks with Cross-Modal Alignment

Chen, G., Liao, Y., Zhang, D., Yang, W., Mai, Z., & Xu, C. F. (2025) · Electronics · doi:10.3390/electronics14183638 · Cited by 9 · see citing works

Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis

Luo, M., Jiang, Y., & Mai, S. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755591 · Cited by 9 · see citing works

Systematic review of recent advances in multimodal sentiment analysis

Baberwal, S. K., Shelke, N. A., & Anwar, K. (2025) · Discover Computing · doi:10.1007/s10791-025-09727-7 · Cited by 12 · see citing works

In Your Face: Sentiment Analysis of Metaphor with Facial Expressive Features

Zhang, D., Zhang, M., Guo, T., Peng, C., Saikrishna, V., & Xia, F. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533972 · Cited by 9 · see citing works

Ring That Bell: A Corpus and Method for Multimodal Metaphor Detection in Videos

Alnajjar, K., Hämäläinen, M. K., & Zhang, S. (2022) · Workshop on Figurative Language Processing (FigLang) · doi:10.18653/v1/2022.flp-1.4 · Cited by 9 · see citing works

TriSAT: Trimodal Representation Learning for Multimodal Sentiment Analysis

Huan, R., Zhong, G., Chen, P., & Liang, R. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3458812 · Cited by 8 · see citing works

Quantum neural networks for multimodal sentiment, emotion, and sarcasm analysis

Singh, J., Bhangu, K. S., Alkhanifer, A. A., AlZubi, A. A., & Ali, F. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.03.023 · Cited by 8 · see citing works

Quantum-Inspired Complex-Valued Language Models for Aspect-Based Sentiment Classification

Zhao, Q., Hou, C., & Xu, R. (2022) · Entropy · doi:10.3390/e24050621 · Cited by 8 · see citing works

Representation distribution matching and dynamic routing interaction for multimodal sentiment analysis

Li, Z., Huang, Z., He, X., Yu, J., Chen, H., Yang, C., & Pan, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113376 · Cited by 7 · see citing works

DAM-Faster RCNN: few-shot defect detection method for wood based on dual attention mechanism

Tong, X., Liang, Z., Qin, M., Liu, F., Yang, J., Xiao, H., & Dai, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-05479-y · Cited by 7 · see citing works

Multimodal model for the Spanish sentiment analysis in a tourism domain

Monsalve-Pulido, J. A., Parra, C., & Aguilar, J. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01202-3 · Cited by 6 · see citing works

QD-MSA : A quantum distributed tensor network framework for multimodal sentiment analysis

Li, Y., Li, Y., Qi, Z., Yang, H., Shang, R., & Jiao, L. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103404 · Cited by 6 · see citing works

A Rearrangement and Restore-Based Mixer Model for Target-Oriented Multimodal Sentiment Classification

Jia, L., Ma, T., Rong, H., Sheng, V. S., Huang, X., & Xie, X. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3341879 · Cited by 6 · see citing works

Multimodal dynamic graph convolutional network for crowdfunding success prediction

Cai, Z.-H., Ding, H., Xu, M., & Cui, X. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111313 · Cited by 6 · see citing works

Quantum-inspired neural networks with stochastic dynamics for multimodal sentiment analysis and sarcasm detection

Yan, K., Lai, P., Zheng, X., Yang, Y., Ren, Y. C., Badarch, T., & Chen, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112923 · Cited by 6 · see citing works

TPFN: A Text-Guided Progressive Fusion Network for Multimodal Sentiment Analysis

Ou, L., & Li, Z. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3663280 · Cited by 5 · see citing works

An Efficient Fusion Mechanism for Multimodal Low-resource Setting

Chauhan, D. S., Ekbal, A., & Bhattacharyya, P. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531900 · Cited by 5 · see citing works

Multimodal Quantum-inspired Network for Emotion Recognition

Xiao, Z., Liao, J. X., Shi, J., Zhang, S., & Li, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130063 · Cited by 4 · see citing works

Kernel mean embedding vs kernel density estimation: A quantum perspective

Berquin, Y. (2024) · Physics Letters A · doi:10.1016/j.physleta.2024.130047 · Cited by 4 · see citing works

Multimodal sentiment analysis of intangible cultural heritage songs with strengthened audio features-guided attention

Fan, T., & Wang, H. (2022) · Journal of Information Science · doi:10.1177/01655515221114454 · Cited by 4 · see citing works

Conditional selection with CNN augmented transformer for multimodal affective analysis

Wang, J., Wang, S., Xiao, S., Lin, R., Dong, M., & Guo, W. (2024) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12320 · Cited by 5 · see citing works

Multi-modal Intent Detection with LVAMoE: the Language-Visual-Audio Mixture of Experts

Li, T., Bao, J., Qin, J., Liang, Y., Zhang, R., & Wang, J. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10688018 · Cited by 4 · see citing works

MST-ARGCN: modality-squeeze transformer with attentional recurrent graph capsule network for multimodal sentiment analysis

Hu, C., Liu, J., Li, X., Li, M., & He, H. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06588-7 · Cited by 4 · see citing works

A gated leaky integrate-and-fire spiking neural network based on attention mechanism for multi-modal emotion recognition

Chen, G., Qian, Z., Qiu, S., Zhang, D., & Zhou, R. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105322 · Cited by 5 · see citing works

A Multi-Granularity Relation Graph Aggregation Framework With Multimodal Clues for Social Relation Reasoning

Xu, C., Chen, F., Jia, Q., Wang, Y., Jin, L., Li, Y., Zhao, Y., & Zhao, C. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543054 · Cited by 4 · see citing works

Natural language processing for extracting consumer sentiment dynamics through multimodal social media analysis to predict microeconomic consumption pattern shifts

Weng, Y., Isleem, H. F., Hindi, K. M. E., & Ezugwu, A. E.-S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01315-2 · Cited by 3 · see citing works

Using Convolutional Neural Networks for the Assessment Research of Mental Health

Liu, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1636855 · Cited by 3 · see citing works

A Comprehensive Survey on Multimodal Sentiment Analysis

Dhal, D., Mohanty, J. P., & Rout, S. S. (2024) · International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT) · doi:10.1109/ic-sit63503.2024.10862066 · Cited by 3 · see citing works

An Entanglement-driven Fusion Neural Network for Video Sentiment Analysis

Gkoumas, D., Li, Q., Yu, Y., & Song, D. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/239 · Cited by 15 · see citing works

A quantum-inspired multimodal sentiment analysis framework

Zhang, Y., Song, D., Zhang, P., Wang, P., Li, J., Li, X., & Wang, B. (2018) · Theoretical Computer Science · doi:10.1016/j.tcs.2018.04.029 · Cited by 107 · see citing works

Towards Multimodal Sentiment Analysis Inspired by the Quantum Theoretical Framework

Li, Q., Stefani, A., Toto, G., Buccio, E. D., & Melucci, M. (2020) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr49039.2020.00044 · Cited by 4 · see citing works

Quantum Cognitively Motivated Context-Aware Multimodal Representation Learning for Human Language Analysis

Gkoumas, D. (2021) · Open Research Online (The Open University) · doi:10.21954/ou.ro.00012a25 · Cited by 2 · see citing works

Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis

Gkoumas, D., Li, Q., Dehdashti, S., Melucci, M., Yu, Y., & Song, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16165 · Cited by 34 · see citing works