# Dynamic Tuning and Multi-Task Learning-Based Model for Multimodal Sentiment Analysis
## 1 Introduction
However, when taking into account these three modalities of data comprehensively, the overall sentiment intensity of the information is \(0\). If we ignore the text information, we might conclude that the sentiment is negative. Conversely, if we overlook the visual information, we might infer that the sentiment is positive. In any case, it is impossible to accurately analyze the overall sentiment of the entire piece of information. Therefore, in the MSA task, single-modality sentiment has a direct impact on the overall sentiment [12; 21; 22]. Nevertheless, many existing methods tend to overlook the significance of unimodal sentiment [18; 23]. Thus,  
Figure 1: **(a)** Critical and secondary modules in the process of optimizing model architecture. Such approaches focus on enhancing the unimodal feature extraction and multimodal feature fusion modules. **(b)** The modules of primary concern in our work. We concentrate on improving model performance by setting appropriate optimization objectives while keeping the structure of other modules unchanged. enabling MSA models to focus on both uni- and multimodal sentiments concurrently has emerged as a formidable challenge in this field. Another challenge faced by MSA tasks lies in their broad range of sentiment ratings. For example, the MOSI dataset requires models to accurately map samples to a sentiment intensity scale of [\(-\)3, +3], which increases the prediction difficulty. Given these challenges, we propose DMMSA, a **D**ynamic Tuning and **M**ulti-Task Learning-Based **MSA** model. DMMSA ensures that the model can capture unimodal signals in detail and integrate multimodal information through the collaborative optimization of unimodal and multimodal tasks. The model is equipped with a text-oriented contrastive learning module to promote feature decoupling and enhance the depth and accuracy of sentiment understanding. Furthermore, incorporating coarse-grained sentiment classification tasks to converge the prediction range improves the accuracy of sentiment intensity determination. We implement Global Dynamic Weight Generation (GDWG) to avoid negative transfer effects and achieve the joint adjustment of model parameters, thereby maximizing overall performance. The main contributions of this paper can be summarized as follows:  
1. We propose the Multi-NT-Xent loss to guide the model in decomposing unimodal features and establishing text-centered contrastive relations. 2. By employing coarse-grained sentiment analysis tasks, we effectively converge the prediction range, reducing the complexity of modeling sentiment intensity. 3. To address the issue of unequal convergence rates among different tasks during multi-task training, we propose the GDWG strategy, effectively mitigating the negative transfer effects arising from such mismatches. Our model is evaluated on three benchmark datasets: CH-SIMS [18], MOSI [24], and MOSEI [23]. The results show that DMMSA outperforms the baseline method in classification and regression tasks when the model structure remains unchanged and only the optimization objectives are replaced. Additionally, we conduct comprehensive ablation studies, substantiating the efficacy of each component within our proposed architecture.