mlsanityData to 14 Oct 2026

Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines

arXiv 1803.07427 ↗First posted 19 Mar 2018cs.CL cs.CV cs.IRcited by 23

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Abstract (arXiv)

We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., role of speaker-exclusive models, importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2018.

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Citing papers per year: 2021: 5; 2022: 9; 2023: 5; 2024: 2; 2025: 2.

23 papers.

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  1. 2022Speech Emotion Recognition using Self-Supervised Featurescited by 53
  2. 2022Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Modulecited by 44
  3. 2022Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalitiescited by 28
  4. 2022Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognitioncited by 16
  5. 2023MultiModN- Multimodal, Multi-Task, Interpretable Modular Networkscited by 14
  6. 2023Emotion fusion for mental illness detection from social media: A surveycited by 13
  7. 2023Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regressioncited by 11
  8. 2021Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shiftscited by 9
  9. 2021Multistage linguistic conditioning of convolutional layers for speech emotion recognitioncited by 8
  10. 2022Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priorscited by 8
  11. 2022Climate and Weather: Inspecting Depression Detection via Emotion Recognitioncited by 8
  12. 2022MMER: Multimodal Multi-task Learning for Speech Emotion Recognitioncited by 7
11 more
  1. 2023One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Datacited by 6
  2. 2021DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalamcited by 4
  3. 2022CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognitioncited by 2
  4. 2022Distribution-based Emotion Recognition in Conversationcited by 2
  5. 2021Sequential Late Fusion Technique for Multi-modal Sentiment Analysiscited by 1
  6. 2024LETS-C: Leveraging Text Embedding for Time Series Classificationcited by 1
  7. 2021Dialog speech sentiment classification for imbalanced datasetscited by 0
  8. 2023Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweetscited by 0
  9. 2024Incomplete Multi-view Multi-label Classification via a Dual-level Contrastive Learning Frameworkcited by 0
  10. 2025Speech Emotion Recognition via Entropy-Aware Score Selectioncited by 0
  11. 2025Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communicationcited by 0