Abstract
Understanding and tracking emotions is vital for improving athlete recovery, performance, and mental wellbeing. Yet, most existing systems lack real time analysis and use overly complex emotion classifications that are hard to apply in practice. This paper presents an emotion analysis system that uses advanced techniques in Artificial Intelligence and Natural Language Processing. A fine-tuned deBERTa-v3 model, trained on the GoEmotions dataset and mapped to seven Ekman emotion categories is used for simplified emotion classification. The users' complete surveys before and after running and the system provides real-time emotion labels with confidence scores. These results are used to generate a dashboard of weekly emotion trends. On a validation split of the official GoEmotions dataset, the model achieved 69.73% accuracy and 61.79% F1 score. The paper also compares this model with baseline methods. The system employs a web-based interface and an API-driven backend to facilitate real-time evaluation of the emotion classification model.