Challenge: M-SENA is an open-source platform for multimodal sentiment analysis.
Approach: They propose to use a platform for multimodal sentiment analysis to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations.
Outcome: The proposed framework provides reliable benchmarks and baseline results of different modality features and MSA benchmarks.

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UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition (2022.emnlp-main)

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Challenge: Existing studies study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two.
Approach: They propose a multimodal sentiment knowledge-sharing framework that unifies MSA and ERC tasks from features, labels, and models.
Outcome: The proposed framework achieves consistent improvements on four public benchmark datasets on MOSI, MOSEI, MELD, and IEMOCAP.
Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis (2021.findings-emnlp)

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Challenge: Recent studies focus on learning cross-modal dynamics, but neglect to explore optimal solution for unimodal networks.
Approach: They propose a new MSA framework to identify contribution of modalities and reduce impact of noisy information.
Outcome: The proposed model outperforms state-of-the-art methods on publicly available datasets.
MPID: A Modality-Preserving and Interaction-Driven Fusion Network for Multimodal Sentiment Analysis (2025.coling-main)

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Challenge: Current methods for multimodal sensing analysis overlook nuanced differences and similarities across modalities, leading to potential biases.
Approach: They propose a Modal-Preserving and Interaction-Driven Fusion Network to address these challenges by integrating text with audio and a separate Adaptive Graded Fusion Module for text and visual data.
Outcome: The proposed model achieves state-of-the-art on CMU-MOSI, CMU -MOSEI, and CH-SIMS datasets.
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)

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Challenge: Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research.
Approach: They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text.
Outcome: The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date.
Mixture of Multimodal Adapters for Sentiment Analysis (2025.naacl-long)

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Challenge: Pre-trained language models (PLMs) have been used for text sentiment analysis but sentiment is hidden in other modalities.
Approach: They propose to fuse emotions from different data to analyze sentiments . they use compression parameter for each expert to reduce training burden .
Outcome: The proposed method achieves state-of-the-art with a tiny trainable parameter count compared to current methods . emotions hidden in body movements or vocal timbres eclipse traditional methods compared with text sentiment analysis .
M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets (2024.lrec-main)

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Challenge: Existing studies on sentiment analysis of tweets focus on the English language . however, there is still a challenge of processing lower-resourced languages .
Approach: They transform tweet sentiment dataset into a multimodal format through a straightforward curation process.
Outcome: The proposed approach performs exceptionally well in unimodal and multimodal configurations.
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)

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Challenge: Multimodal Sentiment Analysis (MSA) is effective when using rich information from multiple sources, but the potential sentiment-irrelevant information across modalities may hinder the performance from being further improved.
Approach: They propose an Adaptive Language-guided Multimodal Transformer (ALMT) that learns an irrelevance/conflict-suppressing representation from visual and audio features under guidance of language features at different scales.
Outcome: The proposed model achieves state-of-the-art on several popular datasets and an abundance of ablation shows the effectiveness of the proposed model.
Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis (2024.findings-emnlp)

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Challenge: Existing methods focus on equally treating the contribution of each modality or statically using text as the dominant modality to conduct interaction, which neglects the situation where each modal may become dominant.
Approach: They propose a Knowledge-Guided Dynamic Modality Attention Fusion Framework (KuDA) that uses sentiment knowledge to guide the model dynamically selecting the dominant modality and adjusting the contributions of each modality.
Outcome: The proposed model can be used to highlight the contribution of dominant modality through the correlation evaluation loss.
Analyzing Modality Robustness in Multimodal Sentiment Analysis (2022.naacl-main)

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Challenge: despite its importance, little attention has been paid to improving the robustness of multimodal models.
Approach: They propose simple diagnostic checks for modality robustness in a trained multimodal model . they find MSA models highly sensitive to a single modality, which creates issues .
Outcome: The proposed checks show that models are highly sensitive to a single modality, which creates issues in their robustness.
InfoEnh: Towards Multimodal Sentiment Analysis via Information Bottleneck Filter and Optimal Transport Alignment (2024.lrec-main)

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Challenge: Existing methods for multi-modal sentiment analysis have been developed to overcome these challenges.
Approach: They propose a method that utilizes a masking technique as the bottleneck for information filtering and integrates all modalities into a common feature space via domain adaptation.
Outcome: Extensive experiments on two benchmark MSA datasets show the proposed method performs better than baselines.

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