KD-MSA: A Multimodal Implicit Sentiment Analysis Approach Based on KAN and Asymmetric Contribution-Aware Dynamic Fusion
Abstract
1. Introduction
- KAN-based feature encoding: We introduce a feature encoder–decoder architecture that replaces the conventional feed-forward network with a KAN module. By leveraging learnable B-spline basis functions, our model captures complex nonlinear relationships that traditional MLP-based encoders fail to represent, thereby improving the expressiveness and interpretability of unimodal sentiment features.
- Sentiment-aware dynamic fusion: We propose a novel cross-fusion strategy guided by dynamically calculated unimodal sentiment weights. Unlike existing static or text-dominant fusion methods, our approach adaptively adjusts modality contributions according to their emotional consistency, effectively suppressing noisy signals and enhancing complementary cues across modalities.
- Integration of multi-head attention: Multi-head attention is incorporated in both unimodal encoding and multimodal fusion stages. This enables the model to capture dependencies from multiple representation subspaces in parallel, improving robustness in handling ambiguous or implicit emotional expressions.
- Comprehensive evaluation with significant improvements: Extensive experiments on two Chinese datasets (CH-SIMS, CH-SIMSv2) and two English datasets (MOSI, MOSEI) demonstrate that our model consistently outperforms strong baselines. Specifically, on CH-SIMSv2, KD-MSA achieves an F1 score of 81.02%, surpassing the best baseline (CENet, 79.63%) by 1.39%. On MOSI, KD-MSA improves the F1 score to 84.87%, a relative gain of 2.64% over MulT. On the large-scale MOSEI dataset, KD-MSA attains an F1 score of 85.89%, exceeding BBFN (85.56%) and MMIM (85.26%), while reducing the MAE to 0.5299, the lowest among all compared models. These results verify the effectiveness and cross-lingual generalization capability of our method.
2. Related Work
2.1. Implicit Sentiment Analysis
2.2. Multimodal Fusion Strategy
3. Methodology
3.1. Task Definition
3.2. Overall Architecture
3.3. Unimodal Feature Extraction
3.4. Encoding with KAN
3.5. Modal Weight Calculation
3.6. Multimodal Dynamic Fusion
3.7. Output and Optimization Objectives
Algorithm 1: Training Procedure of KD-MSA |
4. Experiments
4.1. Datasets
4.2. Evaluation Metrics
4.3. Baselines
4.4. Experimental Settings
4.5. Results and Analysis
4.5.1. Comparison with Baselines
4.5.2. Ablation Study and Analysis
4.5.3. Example Analysis
5. Conclusions and Future Work
- (1)
- Although this paper has achieved asymmetry modeling at the modal level, it has not yet considered more fine-grained modal differences, such as local asymmetry in the temporal dimension or at the semantic segment level. Next, we plan to extend the dynamic fusion mechanism to the local level to more accurately characterize the contribution changes of different modalities in different semantic segments.
- (2)
- We will try to introduce external knowledge resources, such as metaphor knowledge bases, emotional common sense graphs, etc., to enhance the model’s perception of implicit emotional clues, which play an important complementary role in low-resource modalities such as visual media and speech.
- (3)
- The modal asymmetric fusion strategy proposed in this paper will be extended to other implicit sentiment task analyses, such as sarcasm recognition and racial discrimination recognition, to further verify the universality and extensibility of the method.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Datasets | Modality | Size 1 | Len 2 | Dim 3 |
---|---|---|---|---|
CH-SIMS | Text | 39 | 768 | |
Vision | 2281 | 55 | 709 | |
Audio | 400 | 33 | ||
CH-SIMSv2 | Text | 50 | 768 | |
Vision | 4403 | 232 | 177 | |
Audio | 952 | 25 | ||
MOSI | Text | 50 | 768 | |
Vision | 2199 | 500 | 20 | |
Audio | 375 | 5 | ||
MOSEI | Text | 50 | 768 | |
Vision | 22856 | 500 | 35 | |
Audio | 500 | 74 |
Dataset | Train | Valid | Test | Total | Language |
---|---|---|---|---|---|
CH-SIMS | 1368 | 456 | 457 | 2281 | Chinese |
CH-SIMSv2 | 2722 | 647 | 1034 | 4403 | Chinese |
MOSI | 1284 | 229 | 686 | 2199 | English |
MOSEI | 16326 | 1871 | 4659 | 22856 | English |
Parameters | CH-SIMS | CH-SIMSv2 | MOSI | MOSEI |
---|---|---|---|---|
Batch Size | 32 | 32 | 32 | 64 |
Learning Rate | 3 | 3 | 3 | 4 |
Epochs | 100 | 100 | 100 | 100 |
Optimizer | AdamW | AdamW | AdamW | AdamW |
k | 0.1 | 0.3 | 2.0 | 5.0 |
0.01 | 0.01 | 0.01 | 0.1 |
Models | CH-SIMS | CH-SIMSv2 | ||||||
---|---|---|---|---|---|---|---|---|
MAE | Corr | Acc-2 | F1 | MAE | Corr | Acc-2 | F1 | |
LMF † | 0.441 | 0.576 | 0.7777 | 0.7788 | 0.367 | 0.557 | 0.7418 | 0.7388 |
BBFN * | 0.430 | 0.564 | 0.7812 | 0.7788 | 0.300 | 0.708 | 0.7853 | 0.7841 |
CubeMLP * | 0.419 | 0.593 | 0.7768 | 0.7759 | 0.334 | 0.648 | 0.7853 | 0.7853 |
CENet † | 0.471 | 0.534 | 0.7790 | 0.7753 | 0.310 | 0.699 | 0.7956 | 0.7963 |
KD-MSA | 0.4108 | 0.5950 | 0.7877 | 0.7846 | 0.2931 | 0.7365 | 0.8054 | 0.8102 |
Models | MOSI | MOSEI | ||||||
---|---|---|---|---|---|---|---|---|
MAE | Corr | Acc-2 | F1 | MAE | Corr | Acc-2 | F1 | |
TFN † | 0.947 | 0.673 | 0.7908 | 0.7911 | 0.572 | 0.714 | 0.8189 | 0.8174 |
LMF † | 0.950 | 0.651 | 0.7918 | 0.7915 | 0.576 | 0.717 | 0.8348 | 0.8336 |
MulT † | 0.879 | 0.702 | 0.8098 | 0.8095 | 0.559 | 0.733 | 0.8463 | 0.8452 |
MISA † | 0.776 | 0.778 | 0.8354 | 0.8358 | 0.557 | 0.751 | 0.8467 | 0.8466 |
BBFN * | 0.796 | 0.744 | 0.8247 | 0.8244 | 0.545 | 0.760 | 0.8573 | 0.8556 |
MMIM * | 0.744 | 0.780 | 0.8430 | 0.8423 | 0.550 | 0.761 | 0.8542 | 0.8526 |
CubeMLP * | 0.755 | 0.772 | 0.8232 | 0.8423 | 0.537 | 0.761 | 0.8523 | 0.8504 |
KD-MSA | 0.7123 | 0.7975 | 0.8491 | 0.8487 | 0.5299 | 0.7792 | 0.8591 | 0.8589 |
Models | CH-SIMS | MOSI | ||||
---|---|---|---|---|---|---|
MAE | Acc-2 | F1 | MAE | Acc-2 | F1 | |
KD-MSA | 0.4108 | 0.7877 | 0.7846 | 0.7123 | 0.8491 | 0.8487 |
w/o TFKE | 0.4106 | 0.7724 | 0.7696 | 0.7204 | 0.8338 | 0.8340 |
w/o KAND | 0.4113 | 0.7702 | 0.7647 | 0.7399 | 0.8262 | 0.8261 |
w/o DWA | 0.4174 | 0.7549 | 0.7504 | 0.7771 | 0.8262 | 0.8258 |
Example | Truth | Predict | |||||
---|---|---|---|---|---|---|---|
T | A | V | M | MSA * | KD-MSA | ||
Case 1 | 0.6 | −1 | −1 | −0.2 | −0.42 | −0.34 | |
Case 2 | −1 | 0.2 | 1 | 0.6 | 0.95 | 0.84 |
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Hou, Z.; Zhang, Q.; Lei, Z.; Zeng, Z.; Jia, R. KD-MSA: A Multimodal Implicit Sentiment Analysis Approach Based on KAN and Asymmetric Contribution-Aware Dynamic Fusion. Symmetry 2025, 17, 1401. https://doi.org/10.3390/sym17091401
Hou Z, Zhang Q, Lei Z, Zeng Z, Jia R. KD-MSA: A Multimodal Implicit Sentiment Analysis Approach Based on KAN and Asymmetric Contribution-Aware Dynamic Fusion. Symmetry. 2025; 17(9):1401. https://doi.org/10.3390/sym17091401
Chicago/Turabian StyleHou, Zhiyuan, Qiang Zhang, Ziwei Lei, Zheng Zeng, and Ruijun Jia. 2025. "KD-MSA: A Multimodal Implicit Sentiment Analysis Approach Based on KAN and Asymmetric Contribution-Aware Dynamic Fusion" Symmetry 17, no. 9: 1401. https://doi.org/10.3390/sym17091401
APA StyleHou, Z., Zhang, Q., Lei, Z., Zeng, Z., & Jia, R. (2025). KD-MSA: A Multimodal Implicit Sentiment Analysis Approach Based on KAN and Asymmetric Contribution-Aware Dynamic Fusion. Symmetry, 17(9), 1401. https://doi.org/10.3390/sym17091401