Recent years have seen a proliferation of users expressing their feelings on social media sites like Twitter, which has opened the door for real-time sentiment analysis. By distinguishing between distinct emotional states including happiness, fury, sorrow, fear, and surprise, fine-grained emotion recognition surpasses the capabilities of simple sentiment analysis. This research introduces a deep learning method that uses transformers to identify emotions on Twitter with a finer degree of specificity. The system is able to extract intricate linguistic patterns and contextual relationships from brief and noisy social media material by using state-of-the-art models like BERT and RoBERTa. The model is trained and tested using a dataset of pre-processed tweets that have been tagged with emotions. The experimental findings show that when compared to typical machine learning baselines, there is a considerable gain in classification accuracy and F1-score. The suggested model is suitable for practical emotion mining tasks because to its resilience in dealing with language ambiguity, sarcasm, and code-mixed information. Possible uses for this study include social behavior modeling, customer feedback analysis, and mental health monitoring. It also helps to increase AI systems' emotional intelligence.
Kavitha et al. (Mon,) studied this question.