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

arXiv 1606.06259 ↗First posted 20 Jun 2016cs.CL cs.MMcited by 193

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Abstract (arXiv)

People are sharing their opinions, stories and reviews through online video sharing websites every day. Studying sentiment and subjectivity in these opinion videos is experiencing a growing attention from academia and industry. While sentiment analysis has been successful for text, it is an understudied research question for videos and multimedia content. The biggest setbacks for studies in this direction are lack of a proper dataset, methodology, baselines and statistical analysis of how information from different modality sources relate to each other. This paper introduces to the scientific community the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos called Multimodal Opinion-level Sentiment Intensity dataset (MOSI). The dataset is rigorously annotated with labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, and per-milliseconds annotated audio features. Furthermore, we present baselines for future studies in this direction as well as a new multimodal fusion approach that jointly models spoken words and visual gestures.

Key facts. Cited by 193 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2016.

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Citing papers per year: 2021: 24; 2022: 17; 2023: 33; 2024: 39; 2025: 46; 2026: 34.

193 papers.

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  1. 2021SMIL: Multimodal Learning with Severely Missing Modalitycited by 243
  2. 2023emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representationcited by 196
  3. 2021Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysiscited by 179
  4. 2021MultiBench: Multiscale Benchmarks for Multimodal Representation Learningcited by 143
  5. 2022Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questionscited by 116
  6. 2023Emotional Speech-Driven Animation with Content-Emotion Disentanglementcited by 98
  7. 2024Deep Multimodal Learning with Missing Modality: A Surveycited by 89
  8. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
  9. 2023Factorized Contrastive Learning: Going Beyond Multi-view Redundancycited by 73
  10. 2024MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistancecited by 72
  11. 2024ReconBoost: Boosting Can Achieve Modality Reconcilementcited by 56
  12. 2022Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representationscited by 48
38 more
  1. 2021Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space Translationcited by 41
  2. 2021Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysiscited by 41
  3. 2022MIntRec: A New Dataset for Multimodal Intent Recognitioncited by 41
  4. 2021Multimodal End-to-End Sparse Model for Emotion Recognitioncited by 40
  5. 2024Diagnosing and Re-learning for Balanced Multimodal Learningcited by 40
  6. 2021Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributionscited by 39
  7. 2023AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perceptioncited by 39
  8. 2021Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directionscited by 35
  9. 2024Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalitiescited by 32
  10. 2024On-the-fly Modulation for Balanced Multimodal Learningcited by 32
  11. 2025EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Modelscited by 32
  12. 2023A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognitioncited by 29
  13. 2025I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Expertscited by 29
  14. 2025Understanding the Emergence of Multimodal Representation Alignmentcited by 28
  15. 2023Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applicationscited by 27
  16. 2023Multimodal Multi-loss Fusion Network for Sentiment Analysiscited by 26
  17. 2024MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversationscited by 25
  18. 2024Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Contentcited by 25
  19. 2025Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Modelscited by 23
  20. 2021The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvementscited by 22
  21. 2022Defending Multimodal Fusion Models against Single-Source Adversariescited by 20
  22. 2023Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applicationscited by 20
  23. 2022Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysiscited by 18
  24. 2023MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Taskscited by 18
  25. 2024Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learningcited by 18
  26. 2025Adaptive Unimodal Regulation for Balanced Multimodal Information Acquisitioncited by 18
  27. 2021Towards Language-guided Visual Recognition via Dynamic Convolutionscited by 17
  28. 2023UniSA: Unified Generative Framework for Sentiment Analysiscited by 17
  29. 2024AlignMamba: Enhancing Multimodal Mamba with Local and Global Cross-modal Alignmentcited by 17
  30. 2025The iNaturalist Sounds Datasetcited by 17
  31. 2025MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Modelscited by 17
  32. 2024Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Surveycited by 16
  33. 2024Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learningcited by 16
  34. 2023MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Modelscited by 15
  35. 2021DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterancescited by 13
  36. 2023Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignmentcited by 13
  37. 2024Balancing Multimodal Training Through Game-Theoretic Regularizationcited by 13
  38. 2023Multimodal Sentiment Analysis: A Surveycited by 12

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