## 4 Results
### Statistical Analysis of Datasets
These findings underscore the importance of regional prompts in capturing the nuances of language use and how cultural and linguistic diversity are reflected in the generated datasets. From Figure 4, it is evident that both the type-token ratio (TTR) and the counts of hapax legomena were significantly higher in the region-specific detailed datasets compared to the baseline. This indicates that the region-specific prompts resulted in a richer and more diverse vocabulary. Specifically, the higher TTR values suggest that a greater proportion of unique words were used in the regionally tailored datasets, reflecting a broader lexical diversity. Additionally, the increased occurrence of hapax legomena--words that appear only once in the corpus--further supports the notion that these datasets contain a wider range of rare and specialized terms, which are often characteristic of more specific linguistic contexts. These findings imply that the regional prompts successfully encouraged the generation of language that is more varied and representative of the distinct linguistic features and cultural nuances inherent to each region. The higher TTR and hapax legomena counts also suggest that the models trained on these region-specific datasets are better equipped to understand and generate more nuanced expressions reflective of different regional and cultural contexts. This increased linguistic diversity may also indicate that the models trained on these region-specific datasets are better equipped to understand and generate more complex and varied text, moving beyond the generic or repetitive vocabulary typically found in more general datasets. Figure 4: Type–token ration (TTR) and hapax legomena scores for the synthetic baseline and region-specific datasets. These results directly address our first research question (RQ1), which investigates the influence of region-specific prompts on the linguistic diversity and richness of the generated datasets. The pointwise mutual information (PMI) scores for the top 10 co-occurring word pairs from each regional synthetic dataset are presented in Figure 5. These pairs reveal significant associations that are unique to each region, underscoring how regional language variations influence the expression of emotions. The PMI analysis provides valuable insights into how the choice of regional prompts can shape the types of emotional expressions generated by the model, highlighting the subtle but distinct ways in which cultural and linguistic contexts affect the language produced. An interesting observation from this analysis is the variety of emojis and expressions that the language model produces when given appropriate region-specific prompts. The model not only captures regional language nuances but also reflects cultural-specific symbols, like emojis, which are an integral part of modern communication. This suggests that when prompted with regional cues, the model can adapt its output to align with local forms of expression. The analysis of the co-occurring word pairs further illustrates the differences in expressions elicited across the various datasets. These differences are particularly evident in the use of hashtags, a modern cultural feature that encapsulates regional identity. For example, in the UK dataset, hashtags such as #AngryBritish and #ProudBritish are present, directly reflecting the socio-political identity of the region. In contrast, the India dataset includes region-specific hashtags like #IndiaPride, explicitly signifying national pride and cultural identity. Additionally, the dataset from the UK includes subtle references to British cultural elements, such as #RoyallyShocked, which evokes the British royal family, and #TopBanter, a term commonly associated with UK slang. The India dataset, on the other hand, features the hashtag #DesiHigster, a clear cultural marker linked to the youth subculture in India. These cultural nuances, although less overt, provide important context to the emotional expressions generated by the model.