## 1 Introduction
Existing studies have shown that multimodal features can help models learn richer knowledge representations [11; 12]. However, existing pure text sentiment analysis datasets generally lack other modal information. How to effectively transfer multimodal methods to pure text sentiment analysis tasks has become an urgent problem to be solved. To address the aforementioned challenges, this study adds a \"Stable diffusion-based image generation module\" to the original model. Stable diffusion [13], as an advanced image generation technology based on diffusion models, is capable of generating semantically relevant images according to the input text (as shown in Figure 2). By introducing this module, the pure text sentiment analysis task is successfully transformed into a multimodal sentiment analysis task, endowing the model with the ability to \"construct scenes\". Meanwhile, the CLIP model is employed for single-modal feature extraction of both text and images, and the parameters of the CLIP model are frozen during training to reduce training costs. This is because the CLIP model has used 400 million pairs of text-image data in its pre-training phase to achieve deep alignment of text and image data. Therefore, it can achieve good results in downstream tasks without fine-tuning, which is a significant advantage compared with single-modal pre-trained models such as BERT and Swin-Transformer (SWTR) [14]. Based on the above innovations, this study proposes a sentiment analysis model with the ability to \"construct scenes\"--SceEmoNet. This model first uses the Stable diffusion model to generate corresponding visual scenes based on the input text, introducing visual modality information to the model. Subsequently, it leverages the CLIP model to extract aligned multimodal features, effectively addressing the feature discrepancy caused by data heterogeneity. Finally, a late fusion strategy is adopted to integrate information from different modalities to complete the sentiment analysis. Experiments conducted on six different sentiment analysis datasets demonstrate that the SecEmoNet model significantly outperforms the baseline models, with accuracy improvements of 9.57%, 3.87%, 3.63%, 3.14%, 0.77%, and 0.28%, respectively. It also achieves state-of-the-art (SOTA) performance on the offensive dataset. The main contributions of this paper are as follows:  
(1) This study innovatively incorporates an \"image generation module\" into the sentiment analysis model. By generating images that construct scenes consistent with the emotional context of the text content, it enables the model to utilize visual-emotional cues. Experimental validation on six datasets demonstrates that this approach effectively enhances the performance of sentiment analysis. (2) This study systematically examines the influence of three key factors--text pre-processing method selection, generated image quality, and multimodal feature fusion approaches--on the performance of sentiment analysis models, with such impacts validated through experiments. Furthermore, it puts forward feasible improvement strategies  
Figure 1: Examples of human analysis of text sentiments. All figures in the image are AI-generated examples and do not involve any real persons. Figure 2: Examples of images generated using the Stable diffusion model. for future research. These endeavors offer novel insights and methodological references for subsequent studies in related areas of sentiment analysis.