## 1 Introduction  
Emotion detection is a critical task in NLP with applications spanning from sentiment analysis to human-computer interaction. The ability to accurately identify and interpret emotions in text can significantly enhance user experience in various domains, including social media, customer service, and mental health monitoring [1]. Emotions play a central role in human communication, influencing decision making, behavior, and social interactions. Consequently, the accurate detection and analysis of emotions from text have become increasingly important in developing intelligent systems that can understand and respond to human emotions effectively [2]. For instance, in social media, emotion detection can help identify and mitigate cyberbullying, enhance content recommendation systems, and provide insights into public sentiment on various issues [3]. In customer service, emotion-aware systems can improve user satisfaction by tailoring responses to the emotional state of the customer [4]. Moreover, in mental health monitoring, emotion detection can assist in identifying signs of emotional distress and providing timely interventions [5].  
Recent advancements in synthetic dataset generation have opened new avenues for improving the performance of emotion detection models. Synthetic datasets, generated using large language models, offer a scalable solution to the challenges of data scarcity and diversity [6]. However, the effectiveness of these datasets can be influenced by the specificity of the prompts used during their creation. Incorporating regional language nuances into the prompts can potentially enhance the diversity and relevance of the generated datasets, leading to better model performance.  
The primary objectives of this study are to generate synthetic datasets for emotion detection that incorporate regional language variations, analyze the linguistic diversity and regional differences in the generated datasets, and evaluate the impact of region-specific synthetic datasets on the performance of emotion detection models.  
This study makes several key contributions:  
* Demonstrate the feasibility of generating region-specific synthetic datasets for emotion detection using advanced language models.
* Provide a comprehensive analysis of the linguistic diversity and regional differences in the generated datasets.
* Show that region-specific synthetic datasets can significantly improve the performance of emotion detection models.
* The findings highlight the importance of incorporating regional language variations in synthetic dataset generation, offering valuable insights for future research and applications in NLP.  
The novelty of this research lies in its focus on regional language nuances and their impact on synthetic dataset generation and emotion detection performance. By addressing these aspects, the study contributes to the broader understanding of synthetic data's role in NLP and paves the way for more inclusive and effective emotion detection models.  
The remainder of this paper is organized as follows. Section 2 provides an overview of the related work, with subsections covering synthetic dataset generation (Section 2.1), emotion detection (Section 2.2), and regional language variations (Section 2.3). Section 3 details the methodology, including dataset generation (Section 3.3), statistical analysis (Section 3.4), classification experiments (Section 3.5), and evaluation (Section 3.6). Section 4 presents the experimental results, followed by a discussion in Section 5. Finally, Section 6 concludes the study by summarizing key findings and outlining potential directions for future research.