# DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis

arXiv: https://arxiv.org/abs/2412.12225 · HTML: https://mlsanity.com/paper/2412.12225/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [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)
- [Cross Modal Distillation for Supervision Transfer](https://mlsanity.com/paper/1507.00448/) (2015)
- [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)
- [Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities](https://mlsanity.com/paper/1812.07809/) (2018)
- [Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities](https://mlsanity.com/paper/2404.16456/) (2024)
- [WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge](https://mlsanity.com/paper/2401.06659/) (2024)
- [Text-oriented Modality Reinforcement Network for Multimodal Sentiment Analysis from Unaligned Multimodal Sequences](https://mlsanity.com/paper/2307.13205/) (2023)

## Built on by (most cited first, 27 of 27)

- [DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning](https://mlsanity.com/paper/2503.11892/) (2025)
- [Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis](https://mlsanity.com/paper/2602.16144/) (2026)
- [Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2602.19585/) (2026)
- [Motion-Aware Caching for Efficient Autoregressive Video Generation](https://mlsanity.com/paper/2605.01725/) (2026)
- [CLCR: Cross-Level Semantic Collaborative Representation for Multimodal Learning](https://mlsanity.com/paper/2602.19605/) (2026)
- [Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts](https://mlsanity.com/paper/2506.10452/) (2025)
- [Temporal-Spatial Decouple before Act: Disentangled Representation Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2601.13659/) (2026)
- [Group Cognition Learning: Making Everything Better Through Governed Two-Stage Agents Collaboration](https://mlsanity.com/paper/2605.00370/) (2026)
- [Disentangling Bias by Modeling Intra- and Inter-modal Causal Attention for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2508.04999/) (2025)
- [Towards Minimal Causal Representations for Human Multimodal Language Understanding](https://mlsanity.com/paper/2509.21805/) (2025)
- [Calibrating Multimodal Consensus for Emotion Recognition](https://mlsanity.com/paper/2510.20256/) (2025)
- [Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection](https://mlsanity.com/paper/2602.16161/) (2026)
- [CaReFlow: Cyclic Adaptive Rectified Flow for Multimodal Fusion](https://mlsanity.com/paper/2602.19140/) (2026)
- [Uncertainty-Aware Collaborative System of Large and Small Models for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2509.04459/) (2025)
- [Decoupled Hierarchical Distillation for Multimodal Emotion Recognition](https://mlsanity.com/paper/2602.04260/) (2026)
- [ModalImmune: Immunity Driven Unlearning via Self Destructive Training](https://mlsanity.com/paper/2602.16197/) (2026)
- [Addressing Missing and Noisy Modalities in One Solution: Unified Modality-Quality Framework for Low-quality Multimodal Data](https://mlsanity.com/paper/2603.02695/) (2026)
- [Progressive Representation Learning for Multimodal Sentiment Analysis with Incomplete Modalities](https://mlsanity.com/paper/2603.09111/) (2026)
- [Enhance-then-Balance Modality Collaboration for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2604.12518/) (2026)
- [Controlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities](https://mlsanity.com/paper/2605.16889/) (2026)
- [QiNN-QJ: A Quantum-inspired Neural Network with Quantum Jump for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2510.27091/) (2025)
- [GRCF: Two-Stage Groupwise Ranking and Calibration Framework for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2601.09606/) (2026)
- [Breaking the Correlation Plateau: On the Optimization and Capacity Limits of Attention-Based Regressors](https://mlsanity.com/paper/2602.17898/) (2026)
- [Segregate, Refine, Integrate: Decomposing Multimodal Fusion for Sentiment Analysis](https://mlsanity.com/paper/2607.12686/) (2026)
- [Vision-Guided Text Prompt Tuning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2609.06497/) (2026)
- [SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data](https://mlsanity.com/paper/2609.30238/) (2026)
- [Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2609.30470/) (2026)

## Abstract (arXiv)

Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such as language, vision, and audio, to enhance the understanding of human sentiment. While existing models often focus on extracting shared information across modalities or directly fusing heterogeneous modalities, such approaches can introduce redundancy and conflicts due to equal treatment of all modalities and the mutual transfer of information between modality pairs. To address these issues, we propose a Disentangled-Language-Focused (DLF) multimodal representation learning framework, which incorporates a feature disentanglement module to separate modality-shared and modality-specific information. To further reduce redundancy and enhance language-targeted features, four geometric measures are introduced to refine the disentanglement process. A Language-Focused Attractor (LFA) is further developed to strengthen language representation by leveraging complementary modality-specific information through a language-guided cross-attention mechanism. The framework also employs hierarchical predictions to improve overall accuracy. Extensive experiments on two popular MSA datasets, CMU-MOSI and CMU-MOSEI, demonstrate the significant performance gains achieved by the proposed DLF framework. Comprehensive ablation studies further validate the effectiveness of the feature disentanglement module, language-focused attractor, and hierarchical predictions. Our code is available at https://github.com/pwang322/DLF.