## 5 Discussion  
### Linguistic Diversity and Regional Differences  
Our statistical analyses revealed significant regional differences and enhanced linguistic diversity in the datasets generated using detailed prompts. In Figures 7-12, we present the term frequency-inverse document frequency (TF-IDF) word clouds for the different regional datasets. On the left side of these figures, we display the datasets generated using a general prompt, while on the right side, we show the corresponding datasets generated with a more detailed prompt. The word clouds serve as an effective tool for visually comparing the linguistic differences elicited by varying levels of prompt detail. By examining these word clouds, we can quickly identify the specific terms and phrases that are more prominent in each dataset, providing insight into how the detail in the prompt influences the richness and specificity of the generated vocabulary. Figure 7: Top 20 TF-IDF terms in the Regional General US dataset. Figure 8: Top 20 TF-IDF terms in the Regional Detailed US dataset. Figure 10: Top 20 TF-IDF terms in the Regional Detailed UK dataset. Figure 9: Top 20 TF-IDF terms in the Regional General UK dataset. The word clouds from the general prompts typically show more common, broadly used terms, indicating less diversity in the generated content. In contrast, the word clouds from the detailed prompts reveal a wider range of region-specific words and expressions. These differences underscore how detailed prompting leads to more nuanced and contextually appropriate language, reflecting the unique linguistic characteristics of each region. The use of TF-IDF further emphasizes the prominence of these region-specific terms, highlighting how such prompts can better capture cultural and linguistic nuances in the generated data. The observed increase in the type-token ratio (TTR) and hapax legomena ratios in the region-specific datasets compared to the baseline suggests a richer and more diverse vocabulary in the regionally tailored data. This outcome aligns with our hypothesis that the inclusion of region-specific prompts fosters the generation of more varied and nuanced synthetic datasets. The higher diversity in word usage reflects a broader range of emotional expressions, which is crucial for improving the robustness and generalizability of emotion detection models. Furthermore, the pointwise mutual information (PMI) scores for co-occurring word pairs highlighted significant associations unique to each region. These findings underscore the influence of regional language variations on emotional expression, as they show how different cultural and linguistic factors shape the way emotions are conveyed in text. Figure 11: Top 20 TF-IDF terms in the Regional General India dataset. Figure 12: Top 20 TF-IDF terms in the Regional Detailed India dataset. For instance, the use of region-specific idioms, slang, and culturally relevant terms all contribute to the nuanced expression of emotions, which must be accounted for when developing emotion detection systems. In addition, key-phrase extraction via the RAKE algorithm provided valuable insights into the thematic differences across the regional datasets. The identified keyphrases revealed distinctive topics that are of particular relevance to each region, further emphasizing the importance of considering regional context in emotion detection tasks. These regional variations in keyphrases reinforce the idea that emotions are not universally discussed in the same way; instead, they are shaped by local cultural and social factors. By incorporating regional-specific cues, models can achieve better performance and more accurate predictions when dealing with diverse, real-world data. Therefore, these findings highlight the significance of region-specific prompts in capturing the full spectrum of emotional expressions, which is essential for building more effective and inclusive emotion detection systems.