## 5 Conclusions
### Limitations of the Study and Future Research Suggestions  
This study compares the performance of CNN, ViT, and ResNet models in analyzing the emotional emotions of fashion images. Nonetheless, several limitations exist.  
First, the diversity of the dataset is somewhat limited, potentially skewing the results toward specific fashion styles or emotions. Future research should incorporate a larger and more diverse dataset, including varied fashion images and backgrounds. Also, though the dataset consists of practical fashion items in an online shopping service, a relatively smaller dataset may be relatively smaller to limit the generalizability of the results. For the future studies, it can be considered to adopt larger datasets such as ImageNet. Also, the future studies should consider adopting the algorithms and larger datasets to understand and generalize the user's responses--for instance, the impacts of generations, genders, and personal fashion favors.  
Second, although the performance of the models was generally strong, emerging algorithms such as CoAtNet, which combine the local feature extraction capabilities of CNNs and the global information processing strengths of ViTs, have not been sufficiently explored. CoAtNet is a hybrid model that merges the strengths of CNN and ViT, offering enhanced performance in various image analysis tasks. Incorporating CoAtNet in future studies could provide further insights into its effectiveness in fashion image emotion analysis, broadening the scope beyond CNN, ViT, and ResNet.  
Third, this study adopts the analysis environment of Google Colaboratory Lab with the GPU enable option. Nonetheless, in practical business situations, there can be different and diverse system environments affecting the performances. Also, the diversities of the fashion items, such as categories, [PERSON]'s personal scoring results, and the complexities of the images should be considered.  
Fourth, there are diverse performance indices to compare the results of machine learning algorithms. This study mainly considers the loss function results as standing out the performances of each algorithm following the study of [PERSON] et al. because this study converted the emotional scores into 0-1 selection [23]. In future studies, diverse indices can be considered, though.  
Lastly, while this study evaluated model performance using standard metrics such as accuracy, precision, and recall, it did not fully account for subjective user experiences or emotional responses. Future studies should integrate user feedback and subjective evaluation metrics to assess the practical applicability and emotional impact of these models more comprehensively.  
Advancing research in this direction could lead to more refined and practical AI models for fashion image analysis.  
Writing--original draft, G.L. and S.Y.; Writing--review & editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a research grant from Seoul Women's University (2025-0023).  
Not applicable.  
The data presented in this study are available in the corresponding author's database upon request.  
The authors declare no conflict of interest.