## 1 Introduction  
Sentiment analysis is a core research direction in the field of Natural Language Processing (NLP) [1], aiming to use computer technology to parse the sentiment tendencies in texts. It plays a crucial role in scenarios such as public opinion monitoring and user feedback analysis. Early research mainly relied on manually designed feature templates, combined with machine learning algorithms such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM) to implement classification tasks. However, these traditional methods have obvious drawbacks: First, the labor cost is extremely high, as domain experts need to spend a great deal of effort manually designing features. This not only results in a long training cycle but also requires a significant amount of funding. Second, the generalizability is severely lacking. The performance of the model largely depends on the quality of the feature template design, making it difficult to effectively transfer to different application scenarios [3]. With the rapid development of deep learning technologies, models such as Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU) have emerged, achieving automatic extraction of text features. Compared with traditional methods, these deep learning models have shown significant advantages in terms of processing efficiency, cost control, and classification accuracy. In recent years, the introduction of the Transformer [3] architecture and the widespread application of pre-trained models (such as BERT and the GPT series) have further demonstrated the effectiveness of the strategy of \"expanding model scale + large-scale data training\" in text feature extraction [4; 5; 6; 7]. Pre-trained models, by learning the prior knowledge embedded in massive texts, have significantly improved the performance of sentiment analysis tasks. Despite the continuous optimization of classification accuracy through the expansion of model scale and data volume in current research, there is still a key bottleneck: Most studies focus only on the pure text modality and fail to fully consider the \"scene construction\" ability that humans possess when understanding text sentiments. In fact, when humans analyze text sentiments, they often build related scenes in their minds based on past experiences and make comprehensive judgments on emotional tendencies by combining scene information. Cognitive psychology's \"situation model theory\" points out that human understanding of text emotions relies on situational representations of events, characters, and environments [8], which integrate multimodal experiences such as vision and hearing. For example, the metaphor \"his heart is as heavy as a lead block\" can only be accurately interpreted by constructing a scene of depression, while pure text models may only capture the literal meaning of \"lead block\" and fail to perceive the negative emotional intensity. As shown in Figure 1, humans can obtain different results influenced by the constructed scenes in their minds. This limitation is particularly evident in tasks involving ambiguous expressions. For instance, in the irony detection task, unimodal models have low accuracy for sentences such as \"What a lovely day for a piciic\" (uttered on a rainy day), while humans can easily judge the irony by constructing the scene of a rainy-day picnic. Based on this phenomenon, we propose the hypothesis: Can we endow models with a kind of \"imagination\" similar to that of humans by introducing multimodal information? Recent advances in the field of multimodality have provided technical feasibility for this idea. For example, models such as AMFB [9] (for false information detection) and Vistanet [10] (for sentiment analysis) have shown significantly better performance than single-modality models by fusing multimodal features such as visual and textual information.