## 5 Advantages, Limitations, and Future Research Directions of the Model
### Model Limitations and Future Research Directions
#### 5.2.1 Effects of Different Preprocessing Methods on the Model  
Due to the complexity of data, how to process different forms of data so the model can better understand that text information is of great significance for model training. After analyzing the emotion datasets, we selected several different preprocessing methods to process the datasets. The specific preprocessing methods are as follows:  
(1) Method 1: Only the invalid information, like \"@ user\" in the text, is removed. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Trainable Parameter Scale** & **Accuracy** \\ \hline RoBERTa & 125 M & 66.7 \\ BERT-ALL & 109 M & 68.2 \\ \hline BERT-Line & \textless{}1 M (768 \(\times\) k) & 55 \\ CLIP-Line & \textless{}1 M (512 \(\times\) k) & 67.3 \\ SeeEmoNet & \textless{}1 M (512 \(\times\) (2k + 2)) & 70.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Trainable Parameter Sizes Among Models. “k” represents the number of labels. (2) Method 2: Remove \"@ user\" and convert the emojis in the text into text expressions through the demoji tool. (3) Method 3: Remove \"@ user\" and emojis simultaneously. (4) Method 4: Remove all punctuation, \"@ user,\" and emojis. (5) Method 5: Keep emojis; only remove \"@ user\" and punctuation. (6) Method 6: Use back translation technology to convert the original data to Chinese through Google translation and then to English again using Google translation. (7) Method 7: The punctuation marks in the data obtained after processing by method six are deleted. We compare the datasets without processing as the baseline method with the above seven preprocessing methods. The experimental results are shown in the following Table 6.  
We found that the fifth preprocessing method achieved the best results. Comparing Method 4 and Method 5, retaining emojis in text data can make the text better understood by the model. Comparing method 3 and method 4, we can see that removing punctuation also has the same effect. We can see from methods 6 and 7 that the back translation method reduces the accuracy. We analyze the following two reasons:  
(1) The text data processed by the back translation method does not contain emojis. From the above analysis, retaining emojis can improve the quality of the text. (2) After the back translation of complex data, it may lead to a wrong understanding and loss of effective information. As shown in Table 7, the back-translated sentences destroy the critical information \"irritated\" in the sentences, resulting in an incorrect understanding of the model. From Table 6, we can observe that the accuracy difference between the best and worst methods is 4.71%, which indicates that the model's preprocessing methods impact the model's accuracy. In this article, we use method 5 to preprocess all datasets. The selection of preprocessing methods not only affects the quality of the extracted text features but also affects the quality of the generated image, which further affects the quality of image features and fusion features and, finally, has a great impact on the performance of the \begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Accuracy** & \(\Delta\) \\ \hline Baseline Method & 46.66 & 0 \\ Method 1 & 46.59 & \(-\)0.07 \\ Method 2 & 45.67 & \(-\)0.99 \\ Method 3 & 46.38 & \(-\)0.28 \\ Method 4 & 47.22 & +0.56 \\ Method 5 & 47.78 & \(+\)1.12 \\ Method 6 & 43.07 & \(-\)3.59 \\ Method 7 & 44.90 & \(-\)1.76 \\ \hline \end{tabular}
\end{table} 
Table 6: Experimental results of inputting text processed by different preprocessing methods into the CLIP model. \begin{table}
\begin{tabular}{c c} \hline Original text & May or may not have just \\  & pulled the legal card on \\  & these folks. \#irritated \\ Text after back translation & Maybe may not only drag \\  & legal cards on these \\  & people. \#Rirritated \\ \hline \end{tabular}
\end{table} 
Table 7: Example of text preprocessing. model. Our proposed method is more sensitive to selecting text preprocessing methods than the traditional text classification model.