## 4 Results
### Statistical Analysis of Datasets
In the US dataset, more region-specific cultural references are observed, such as \"Eggo waffles\" and the hashtag #SayNoToPineapplePizza, both of which highlight Ameri- can food culture and regional humor. Similarly, the UK dataset features references to \"tea\",  
Figure 5: Top PMI sample pairs from different synthetic regional datasets. Usage of different emojis highlights the emotion loaded content in the generated texts. a beverage deeply rooted in British tradition, while the India dataset includes \"vegan\", reflecting growing trends in Indian food culture. These examples illustrate how socio-cultural aspects, particularly food, are reflected in the synthetic datasets, offering further evidence of the model's sensitivity to regional influences. This detailed analysis of co-occurring word pairs and regional expressions provides crucial insights into the second research question (RQ2), which explores the influence of regional prompts on the expression of emotions and the way cultural elements shape the language model's output. By observing these region-specific linguistic features, deeper understanding of how models can be fine-tuned to capture and reflect cultural nuances in emotional expressions was gained. To further investigate the variations in the generated content, keyphrase extraction was conducted using the rapid automatic keyword extraction (RAKE) algorithm. This method allowed us to identify prominent topics within each dataset, revealing thematic differences across regions. The top 10 keyphrases for each dataset are presented in Figure 6. One of the first observations from this analysis is that the baseline dataset contains highly repetitive keyphrases, with almost all of the top ten phrases being identical. This suggests a lack of linguistic diversity in the baseline dataset, which likely arises from the general nature of the prompts used to generate the content. In contrast, the regional datasets--corresponding to the IN (India), US, and UK regions--are presented on the left side of Figure 6. These datasets were generated using a general prompt, and as expected, many of the keyphrases seem to follow a templated structure. Although there is some regional specificity reflected in the keyphrases, such as the inclusion of cultural references and colloquialisms, the overall linguistic diversity appears somewhat limited due to the broad nature of the prompting. On the right side of Figure 6, the keyphrases extracted from datasets generated with more specific prompts are presented. These datasets exhibit a much greater variety in terms of linguistic expression, with the keyphrases being more free-form and less constrained by a predefined structure. For example, the India dataset reveals the interesting feature of  
Figure 6: Top 10 keyphrases extracted by RAKE from each synthetic regional dataset. The usage of different emojis highlights the emotions one the generated texts. code-switching, with the use of local languages interspersed with English terms. Reflecting the cultural and linguistic fluidity present in Indian communication. These differences highlight the power of prompt design in eliciting region-specific linguistic features. By using more specific prompts, it is possible to generate content that is not only richer in variety but also more authentic to the cultural context of the region. This underscores the importance of tailoring prompts to capture the linguistic diversity of different regions, which can significantly influence the quality and authenticity of the generated content. Ultimately, this analysis further demonstrates how prompt engineering can be used to steer the generation of language models toward more regionally relevant outputs. In summary, the results of our experiments demonstrate that incorporating regional language variations into synthetic dataset generation significantly enhances the performance of emotion detection models. The detailed prompts, in particular, provide a richer and more diverse linguistic representation, leading to improved model accuracy and robustness.