## 6 Conclusions  
In this study, the impact of region-specific synthetic datasets on emotion detection performance was explored. By leveraging GPT-3.5, synthetic datasets were generated and tailored to distinct English variations from the US, UK, and India, alongside a baseline dataset. Our methodology incorporated two levels of prompt specificity to evaluate how regional language nuances influenced the diversity and relevance of the generated datasets. This approach enabled us to assess the extent to which linguistic and cultural variations shape emotion detection tasks. Our comprehensive statistical analyses, including frequency distribution, TF-IDF, type-token ratio, hapax legomena, PMI scores, and keyphrase extraction, revealed notable regional differences and enhanced linguistic diversity in datasets generated with more detailed prompts. These findings underscore the importance of incorporating regional language variations in synthetic dataset generation, as they contribute to a richer and more diverse representation of language, enhancing the quality of models trained on such data. The classification experiments using BERT and BART models demonstrated that region-specific synthetic datasets, particularly those generated with detailed prompts, led to significant improvements in emotion detection performance compared to the baseline. This improvement highlights the potential of region-specific datasets to enhance the robustness and accuracy of emotion detection models. The results also address the limitations of the current approaches, which often fail to capture the intricacies of regional language variations that can significantly affect emotion recognition. Our research contributes to the broader understanding of synthetic data's role in natural language processing by systematically evaluating the impact of regional language variations. Valuable insights into how synthetic datasets can be tailored to better reflect linguistic diversity were gained, ultimately making emotion detection models more robust and accurate. Additionally, this study opens up new avenues for future research, suggesting that the methodology can be expanded to include other languages, regions, and more fine-grained regional distinctions. By doing so, researchers can enhance the applicability and effectiveness of synthetic datasets across various NLP tasks. In summary, our findings demonstrate that incorporating regional language nuances into synthetic dataset generation is a promising and effective approach to improving emotion detection models. This research not only advances the field of synthetic data generation but also emphasizes the importance of developing inclusive and representativedatasets that reflect the full spectrum of linguistic and cultural diversity. Future work can build on this foundation by exploring additional regions and languages, with the ultimate goal of creating more robust, accurate, and culturally aware NLP systems that can better serve global, multilingual audiences. This research was funded by the National Science and Technology Council of Taiwan grant number NSTC 113-2221-E-030-006. The funding agreement ensured the author's independence in designing the study, interpreting the data, writing, and publishing the report. Not applicable. The datasets generated and analyzed during the current study are available in the GitHub repository at the following link: [[https://github.com/fhcalderon87/SyntheticRegionalEmotion](https://github.com/fhcalderon87/SyntheticRegionalEmotion)]([https://github.com/fhcalderon87/SyntheticRegionalEmotion](https://github.com/fhcalderon87/SyntheticRegionalEmotion)) (accessed on 1 December 2024). This repository includes the synthetic datasets for emotion detection tailored to English variations from the US, UK, and India, as well as the baseline dataset. Additional data and code used for statistical analyses and classification experiments are also provided to ensure reproducibility and facilitate further research.