Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Mid-term fusion typically employs attention mechanisms, feature weighting, connectivity, or GNNs to combine modality-specific features.
“This method typically employs attention mechanisms, feature weighting, connectivity, or GNNs to combine modality specific features [41,42].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [42]
Cites
1 retracted
Employing Multimodal Machine Learning for Stress Detection
Walambe, R. A., Nayak, P., Bhardwaj, A., & Kotecha, K. V. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/9356452 · Cited by 68 · see citing works
RetractedRecognizing contextual polarity in phrase-level sentiment analysis
Wilson, T., Wiebe, J., & Hoffmann, P. (2005) · doi:10.3115/1220575.1220619 · Cited by 3,388 · 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
Self-citationA review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644 · see citing works
Self-citationMultimodal 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
Self-citationMELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., & Mihalcea, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1050 · Cited by 1,416 · see citing works
Self-citationProceedings of the 29th ACM International Conference on Multimedia
Yue, J., Li, H., Wei, P., Li, G., & Lin, L. (2021) · doi:10.1145/3474085 · Cited by 927 · see citing works
CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope
Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., & Ghayvat, H. (2021) · Electronics · doi:10.3390/electronics10202470 · Cited by 890 · see citing works
Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon
Mohammad, S. M., & Turney, P. D. (2010) · NPARC · Cited by 882 · 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
Self-citationYu, W., Xu, H., Yuan, Z., & Wu, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17289 · Cited by 816 · see citing works
A survey of multimodal sentiment analysis
Soleymani, M., García, D., Jou, B., Schuller, B. W., Chang, S., & Pantić, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.08.003 · Cited by 620 · see citing works
Strapparava, C., & Mihalcea, R. F. (2007) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/1621474.1621487 · Cited by 602 · see citing works
Han, W., Chen, H., & Poria, S. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.723 · Cited by 570 · see citing works
Self-citationPoria, S., Cambria, E., & Gelbukh, A. F. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1303 · Cited by 553 · see citing works
Self-citationA review of natural language processing techniques for opinion mining systems
Sun, S., Luo, C., & Chen, J. (2016) · Information Fusion · doi:10.1016/j.inffus.2016.10.004 · Cited by 546 · 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
Self-citationFusing audio, visual and textual clues for sentiment analysis from multimodal content
Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.01.095 · Cited by 534 · see citing works
Self-citationEmotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances
Poria, S., Majumder, N., Mihalcea, R. F., & Hovy, E. H. (2019) · IEEE Access · doi:10.1109/access.2019.2929050 · Cited by 532 · see citing works
Self-citationTowards multimodal sentiment analysis: harvesting opinions from the web
Morency, L.-P., Mihalcea, R., & Doshi, P. (2011) · Proceedings of the 13th international conference on multimodal interfaces · doi:10.1145/2070481.2070509 · Cited by 470 · see citing works
Youtube movie reviews: Sentiment analysis in an audio-visual context
Wollmer, M., Weninger, F., Knaup, T., Schuller, B., Sun, C., Sagae, K., & Morency, L.-P. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.34 · Cited by 403 · see citing works
Multimodal sentiment analysis using hierarchical fusion with context modeling
Majumder, N., Hazarika, D., Gelbukh, A. F., Cambria, E., & Poria, S. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.07.041 · Cited by 385 · see citing works
Self-citationDeep learning for affective computing: Text-based emotion recognition in decision support
Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S., & Prendinger, H. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.09.002 · Cited by 371 · see citing works
Fighting an Infodemic: COVID-19 Fake News Dataset
Patwa, P., Sharma, S., Pykl, S., Guptha, V., Kumari, G., Akhtar, M. S., Ekbal, A., Das, A., & Chakraborty, T. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-73696-5_3 · Cited by 357 · see citing works
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
Poria, S., Hazarika, D., Majumder, N., & Mihalcea, R. F. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3038167 · Cited by 344 · see citing works
Self-citationFacial emotion recognition using multi-modal information
De Silva, L. C., Miyasato, T., & Nakatsu, R. (2002) · Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat. No.97TH8237) · doi:10.1109/icics.1997.647126 · Cited by 319 · see citing works
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Wöllmer, M., Kaiser, M., Eyben, F., Schuller, B., & Rigoll, G. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.03.001 · Cited by 288 · see citing works
Opinion Mining and Information Fusion: A survey
Balazs, J., & Velásquez, J. D. (2015) · Information Fusion · doi:10.1016/j.inffus.2015.06.002 · Cited by 253 · see citing works
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)
Castro, S., Hazarika, D., Pérez-Rosas, V., Zimmermann, R., Mihalcea, R., & Poria, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1455 · Cited by 243 · see citing works
Self-citationTowards an intelligent framework for multimodal affective data analysis
Poria, S., Cambria, E., Hussain, A., & Huang, G.-B. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.10.005 · Cited by 236 · see citing works
Self-citationMultimodal 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
Multimodal Sentiment Analysis of Spanish Online Videos
Rosas, V. P., Mihalcea, R. F., & Morency, L. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.9 · Cited by 228 · see citing works
2021 International Joint Conference on Neural Networks (IJCNN)
(2021) · doi:10.1109/ijcnn52387.2021 · Cited by 215 · see citing works
Driver stress detection via multimodal fusion using attention-based CNN-LSTM
Mou, L., Zhou, C., Zhao, P., Nakisa, B., Rastgoo, M. N., Jain, R., & Gao, W. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114693 · Cited by 199 · see citing works
SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!
Sharma, C., Bhageria, D., Scott, W., Pykl, S., Das, A., Chakraborty, T., Pulabaigari, V., & Gambäck, B. (2020) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2020.semeval-1.99 · Cited by 174 · 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
Self-citationContext-Sensitive Learning for Enhanced Audiovisual Emotion Classification
Metallinou, A., Wöllmer, M., Katsamanis, A., Eyben, F., Schuller, B. W., & Narayanan, S. S. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.40 · Cited by 154 · see citing works
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
Quantum-inspired multimodal fusion for video sentiment analysis
Li, Q., Gkoumas, D., Lioma, C., & Melucci, M. (2021) · Information Fusion · doi:10.1016/j.inffus.2020.08.006 · Cited by 143 · 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
Zhang, Y., Song, D., Li, X., Zhang, P., Wang, P., Rong, L., Yu, G., & Wang, B. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.04.003 · Cited by 114 · 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
A novel context-aware multimodal framework for persian sentiment analysis
Dashtipour, K. K., Gogate, M., Cambria, E., & Hussain, A. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.020 · Cited by 101 · see citing works
Self-citationLearning 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
Stappen, L., Baird, A., Schumann, L., & Schuller, B. W. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3097002 · Cited by 92 · see citing works
Mai, S., Xing, S., & Hu, H. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3068598 · Cited by 89 · see citing works
Context- and Sentiment-Aware Networks for Emotion Recognition in Conversation
Tu, G., Wen, J., Liu, C., Jiang, D., & Cambria, E. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3149234 · Cited by 86 · see citing works
Self-citationLearning binary codes with neural collaborative filtering for efficient recommendation systems
Li, Y., Wang, S., Pan, Q., Peng, H., Yang, T., & Cambria, E. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.02.012 · Cited by 81 · see citing works
Self-citationPredicting Personality Traits using Multimodal Information
Alam, F., & Riccardi, G. (2014) · doi:10.1145/2659522.2659531 · Cited by 78 · see citing works
Mining patient opinion to evaluate the service quality in healthcare: a deep-learning approach
Shah, A. M., Yan, X., Shah, S. A. A., & Mamirkulova, G. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01434-8 · Cited by 75 · see citing works
Du, Y., Li, T., Pathan, M. S., et al. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09832-x · Cited by 72 · see citing works
Social Image Sentiment Analysis by Exploiting Multimodal Content and Heterogeneous Relations
Xu, J. J., Li, Z., Huang, F., Li, C., & Yu, P. S. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3005405 · Cited by 70 · see citing works
A cognitive brain model for multimodal sentiment analysis based on attention neural networks
Li, Y., Zhang, K., Wang, J., & Gao, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.021 · Cited by 63 · see citing works
Multimodal Sentiment Analysis Using Multi-tensor Fusion Network with Cross-modal Modeling
Yan, X., Xue, H., Jiang, S., & Liu, Z. (2021) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.2000688 · Cited by 57 · see citing works
Huddar, M. G., Sannakki, S. S., & Rajpurohit, V. S. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10285-x · Cited by 55 · see citing works
SWAFN: Sentimental Words Aware Fusion Network for Multimodal Sentiment Analysis
Chen, M., & Li, X. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.93 · Cited by 54 · see citing works
Open-plan office noise is stressful: multimodal stress detection in a simulated work environment
Sander, L., Marques, C., Birt, J., Stead, M., & Baumann, O. (2021) · Journal of Management & Organization · doi:10.1017/jmo.2021.17 · Cited by 49 · see citing works
Sentiment-influenced trading system based on multimodal deep reinforcement learning
Chen, Y., & Huang, S.-H. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107788 · Cited by 48 · see citing works
Ensemble application of ELM and GPU for real-time multimodal sentiment analysis
Tran, H.-N., & Cambria, E. (2017) · Memetic Computing · doi:10.1007/s12293-017-0228-3 · Cited by 47 · see citing works
Self-citationCMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French
Zadeh, A. B., Cao, Y., Hessner, S., Liang, P. P., Poria, S., & Morency, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.141 · Cited by 46 · see citing works
Self-citationA review on sentiment discovery and analysis of educational big‐data
Han, Z., Wu, J., Huang, C., Huang, Q., & Zhao, M. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1328 · Cited by 44 · see citing works
Aloshban, N., Esposito, A. A., & Vinciarelli, A. (2021) · Cognitive Computation · doi:10.1007/s12559-020-09808-3 · Cited by 41 · see citing works
Kalman Filter Based Classifier Fusion for Affective State Recognition
Glodek, M., Reuter, S., Schels, M., Dietmayer, K., & Schwenker, F. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38067-9_8 · Cited by 39 · see citing works
Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter
Masud, S., Dutta, S., Makkar, S., et al. (2021) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde51399.2021.00050 · Cited by 39 · see citing works
Multimodal Analysis and Prediction of Persuasiveness in Online Social Multimedia
Park, S., Shim, H. S., Chatterjee, M., Sagae, K., & Morency, L. (2016) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2897739 · Cited by 38 · see citing works
An AutoML-based Approach to Multimodal Image Sentiment Analysis
Lopes, V., Gaspar, A., Alexandre, L. A., & Cordeiro, J. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533552 · Cited by 37 · see citing works
Hussain, Z., Sheikh, Z., Tahir, A., Dashtipour, K. K., Gogate, M., Sheikh, A., & Hussain, A. (2022) · JMIR Public Health and Surveillance · doi:10.2196/32543 · Cited by 37 · see citing works
Self-citationDeception detection using multimodal fusion approaches
Chebbi, S., & Jebara, S. B. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11148-9 · 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
An Ensemble Method for Radicalization and Hate Speech Detection Online Empowered by Sentic Computing
Araque, Ó., & Iglesias, C. Á. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09845-6 · Cited by 34 · see citing works
Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification
Sharma, R., Bhattacharyya, P., Dandapat, S., et al. (2018) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p18-1089 · Cited by 32 · see citing works
A novel multimodal fusion network based on a joint-coding model for lane line segmentation
Zou, Z., Zhang, X., Liu, H., Li, Z., Hussain, A., & Li, J. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.10.008 · Cited by 27 · see citing works
Self-citationDynamic 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
Social Media Sentiment Analysis Based on Dependency Graph and Co-occurrence Graph
Jin, Z., Tao, M., Zhao, X., & Hu, Y. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10004-8 · Cited by 25 · see citing works
Proceedings of the 4th International Conference on Machine Learning and Soft Computing
Huynh, H. X., Trung, D. T., Thi, O. L. M., et al. (2020) · doi:10.1145/3380688 · Cited by 24 · see citing works
Two-Level Multimodal Fusion for Sentiment Analysis in Public Security
Sun, J., Yin, H., Tian, Y., Wu, J., Shen, L., & Chen, L. (2021) · Security and Communication Networks · doi:10.1155/2021/6662337 · Cited by 23 · see citing works
Multimodal Sentiment Analysis: Review, Application Domains and Future Directions
Gandhi, A., Adhvaryu, K., & Khanduja, V. (2021) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon52575.2021.9686504 · Cited by 21 · see citing works
Self-citationMultimodal Sentiment Analysis of #MeToo Tweets using Focal Loss (Grand Challenge)
Basu, P., Tiwari, S., Mohanty, J., & Karmakar, S. (2020) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm50055.2020.00076 · Cited by 19 · see citing works
Multimodal Analytics for Next-Generation Big Data Technologies and Applications
Seng, K. P., Ang, L.-M., Liew, A. W., et al. (2019) · Springer eBooks · doi:10.1007/978-3-319-97598-6 · Cited by 18 · see citing works
Multimodal sentiment analysis applied to interaction between patients and a humanoid robot Pepper
Rozanska, A., & Podpora, M. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.12.696 · Cited by 16 · see citing works
Multimodal sentiment analysis with asymmetric window multi-attentions
Lai, H., & Yan, X. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11234-y · Cited by 15 · see citing works
SFNN: Semantic Features Fusion Neural Network for Multimodal Sentiment Analysis
Wu, W., Wang, Y., Xu, S., & Yan, K. (2020) · International Conference on Automation, Control and Robotics Engineering (CACRE) · doi:10.1109/cacre50138.2020.9230015 · Cited by 13 · see citing works
Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection
Park, J., Kim, M.-H., & Choi, D. (2021) · Electronics · doi:10.3390/electronics10070800 · Cited by 9 · see citing works
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis
Wang, X., He, J., Jin, Z., Yang, M., Wang, Y., & Qu, H. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114794 · Cited by 6 · see citing works
Alqahtani, A. S., Pandiaraj, S., Murali, M., Alshmrany, S., & Al-SARRAYRIH, H. S. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6259 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Effective Techniques for Multimodal Data Fusion: A Comparative Analysis
Pawłowski, M., Wróblewska, A., & Sysko-Romańczuk, S. (2023) · Sensors · doi:10.3390/s23052381 · Cited by 180 · see citing works
Fan, H., Zhang, X., Xu, Y., Fang, J., Zhang, S., Zhao, X., & Yu, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102161 · Cited by 137 · see citing works
Hate speech detection: A comprehensive review of recent works
Gandhi, A., Ahir, P., Adhvaryu, K., Shah, P., Lohiya, R., Cambria, E., Poria, S., & Hussain, A. (2024) · Expert Systems · doi:10.1111/exsy.13562 · Cited by 66 · see citing works
Self-citationMultimodal fusion and vision–language models: A survey for robot vision
Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 41 · see citing works
- Authors
- Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A.
- Affiliations
- Gujarat Technological University (IN)
- Singapore University of Technology and Design (SG)
- Nanyang Technological University (SG)
- Edinburgh Napier University (GB)
- Corresponding author
- Gandhi, Ankita
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 91
- Pages
- 424-444
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W4297499129
- Cited by
- 783
- References
- 88
- Citations by year
- 2026: 158 · 2025: 325 · 2024: 215 · 2023: 81 · 2022: 4
- FWCI
- 11.00
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Information Fusion (publishedVersion)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (acceptedVersion, other-oa)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- Ministry of Education - Singapore (MOE-T2EP20220–0017)
- Engineering and Physical Sciences Research Council (EP/M026981/1, EP/T021063/1, EP/T024917/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- multimodal sentiment analysis, multimodal fusion, feature-level fusion, decision fusion, hybrid fusion, attention fusion, tensor fusion, hierarchical fusion, polarity classification, multimodal feature extraction, video sentiment analysis, sentiment datasets
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review