# M-SENA: An Integrated Platform for Multimodal Sentiment Analysis

arXiv: https://arxiv.org/abs/2203.12441 · HTML: https://mlsanity.com/paper/2203.12441/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 59 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Efficient Low-rank Multimodal Fusion with Modality-Specific Factors](https://mlsanity.com/paper/1806.00064/) (2018)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2102.04830/) (2021)
- [Learning Factorized Multimodal Representations](https://mlsanity.com/paper/1806.06176/) (2018)
- [Memory Fusion Network for Multi-view Sequential Learning](https://mlsanity.com/paper/1802.00927/) (2018)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2109.00412/) (2021)
- [Multi-attention Recurrent Network for Human Communication Comprehension](https://mlsanity.com/paper/1802.00923/) (2018)
- [Rendering of Eyes for Eye-Shape Registration and Gaze Estimation](https://mlsanity.com/paper/1505.05916/) (2015)
- [Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition](https://mlsanity.com/paper/2110.06309/) (2021)
- [Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis](https://mlsanity.com/paper/2107.13669/) (2021)
- [Multimodal End-to-End Sparse Model for Emotion Recognition](https://mlsanity.com/paper/2103.09666/) (2021)
- [Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization](https://mlsanity.com/paper/1907.01011/) (2019)
- [Benchmarking Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.09538/) (2017)
- [A Multimodal Emotion Sensing Platform for Building Emotion-Aware Applications](https://mlsanity.com/paper/1903.12133/) (2019)

## Built on by (most cited first, 21 of 21)

- [UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2211.11256/) (2022)
- [Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module](https://mlsanity.com/paper/2209.02604/) (2022)
- [Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2310.05804/) (2023)
- [Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2208.07589/) (2022)
- [Noise-Resistant Multimodal Transformer for Emotion Recognition](https://mlsanity.com/paper/2305.02814/) (2023)
- [End-to-end Semantic-centric Video-based Multimodal Affective Computing](https://mlsanity.com/paper/2408.07694/) (2024)
- [A Principled Framework for Multi-View Contrastive Learning](https://mlsanity.com/paper/2507.06979/) (2025)
- [SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method](https://mlsanity.com/paper/2305.01954/) (2023)
- [DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis](https://mlsanity.com/paper/2504.11082/) (2025)
- [Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2210.15824/) (2022)
- [Impact of Stickers on Multimodal Sentiment and Intent in Social Media: A New Task, Dataset and Baseline](https://mlsanity.com/paper/2405.08427/) (2024)
- [Evaluation of data inconsistency for multi-modal sentiment analysis](https://mlsanity.com/paper/2406.03004/) (2024)
- [MVP: Multimodal Emotion Recognition based on Video and Physiological Signals](https://mlsanity.com/paper/2501.03103/) (2025)
- [Disentangling Bias by Modeling Intra- and Inter-modal Causal Attention for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2508.04999/) (2025)
- [PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2312.12334/) (2023)
- [DashFusion: Dual-stream Alignment with Hierarchical Bottleneck Fusion for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2512.05515/) (2025)
- [Progressive Representation Learning for Multimodal Sentiment Analysis with Incomplete Modalities](https://mlsanity.com/paper/2603.09111/) (2026)
- [Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture](https://mlsanity.com/paper/2505.04642/) (2025)
- [GRAMformer: Any-Order Modality Interactions via Volumetric Multimodal Cross-Attention](https://mlsanity.com/paper/2606.06249/) (2026)
- [Segregate, Refine, Integrate: Decomposing Multimodal Fusion for Sentiment Analysis](https://mlsanity.com/paper/2607.12686/) (2026)
- [MIDAS: Mutual Information Disentanglement with Uncertainty-Aware Fusion for Incomplete Multimodal Sentiment Analysis](https://mlsanity.com/paper/2608.09986/) (2026)

## Abstract (arXiv)

M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules. In this paper, we first illustrate the overall architecture of the M-SENA platform and then introduce features of the core modules. Reliable baseline results of different modality features and MSA benchmarks are also reported. Moreover, we use model evaluation and analysis tools provided by M-SENA to present intermediate representation visualization, on-the-fly instance test, and generalization ability test results. The source code of the platform is publicly available at https://github.com/thuiar/M-SENA.