## 4 Experiments
### Comparative Experiment  
To demonstrate the effectiveness of our proposed method, we conducted comparative experiments on six different task datasets with seven models: SVM, LSTM, FastText, RoBERTa, BERT-Line, BERT-ALL, and CLIP-Line. The results for SVM, LSTM, FastText, and RoBERTa were obtained from the official benchmarks [[https://github.com/cardiffnlp/tweeteval?tab=readme-ov-file](https://github.com/cardiffnlp/tweeteval?tab=readme-ov-file)]([https://github.com/cardiffnlp/tweeteval?tab=readme-ov-file](https://github.com/cardiffnlp/tweeteval?tab=readme-ov-file)) (accessed on 29 July 2025). The results for BERT-Line, BERT-ALL, CLIP-Line, and SeeEmoNet were calculated as the average values from five runs of our experiments. The specific configurations are as follows:  
BERT-Line: During training, we froze all parameters of the BERT model and only updated the final classifier. BERT-ALL: We used the traditional BERT fine-tuning method with the maximum text length set to 512. CLIP-Line: We froze the text encoder of the CLIP model and only updated the final classifier. SeeEmoNet: The model proposed in this paper. The experimental results are shown in Table 3.  
Firstly, as shown in the table, the SeeEmoNet model proposed in this paper has achieved state-of-the-art (SOTA) results compared with the baseline models. Compared with the CLIP-Line model without the image generation module, SeeEmoNet has shown significant improvements in performance across all six tasks, with an average increase of 3.5%. This demonstrates that the method proposed in this paper, which endows the model with the ability to \"construct scenes\" by incorporating an image generation module into the text classification model, is effective in enhancing model performance. Secondly, it can be observed from the table that the BERT-ALL model has an average performance im \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task Type** & **Training** & **Validate** & **Test** & **Task Types** \\ \hline Emotion & 3257 & 374 & 1421 &
\begin{tabular}{c} quaternary \\ classification \\ binary classification \\ binary classification \\ binary classification \\ binary classification \\ binary classification \\ binary classification \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The specific distribution of data in the experimental datasets. provement of 0.9% compared with CLIP-Line. However, when compared with SecEmoNet, its performance drops by 2.6%. This indicates that the method proposed in this paper can effectively mitigate the issue of insufficient model learning caused by the freezing of pre-trained model parameters. Lastly, by comparing CLIP-Line and BERT-Line, we find that CLIP-Line has a performance improvement of nearly 13%, which further validates that the CLIP model has stronger zero-shot capabilities compared with BERT. To further verify whether the performance improvement of SecEmoNet is statistically significant, we conducted an analysis using paired \(t\)-tests, comparing the performance differences between the proposed SecEmoNet model and two similar baseline models, namely CLIP-Line and BERT-Line. The test threshold was set at 0.05, and the specific results are shown in Table 4.  
As can be seen from the data in the table, the \(p\)-values obtained after paired \(t\)-tests between SecEmoNet and CLIP-Line, as well as between SecEmoNet and BERT-Line, are all less than 0.05, which fully demonstrates that the performance improvement of this model is statistically significant. To verify the high cost-effectiveness of the SecEmoNet model, we compared its trainable parameter scale and accuracy with those of various baseline models, and the results are presented in Table 5.  
As shown in Table 5, among models with trainable parameter scales in the same \"less than 1 M\" magnitude, SecEmoNet ranks first with an accuracy rate of 70.8%. This result indicates that the proposed SecEmoNet model has a greater advantage in parameter utilization efficiency, being able to achieve more accurate task performance with streamlined parameters.