Published Paper Details:

Emotion recognition from text is a critical task in Natural Language Processing (NLP), with wideranging applications in sentiment analysis, mental health monitoring, and human-computer interaction. Its investigates the application of Bidirectional Encoder Representations from Transformers (BERT) for emotion recognition. BERT's ability to capture contextual dependencies and nuances in text, through its bidirectional transformer architecture, makes it ideal for this task. By fine-tuning pre-trained BERT models on an emotionlabeled dataset, we classify emotions such as happiness, sadness, anger, surprise, fear, and others. The model leverages BERT's pre-trained knowledge of language and its capacity to understand complex syntactic and semantic patterns. Experimental evaluations show that BERT achieves superior accuracy in detecting emotions compared to traditional machine learning models like SVM and logistic regression, due to its deep contextual embeddings. Furthermore, our approach improves generalization across varied emotional expressions, demonstrating BERT's robustness in emotion-aware NLP tasks. These results suggest that BERT-based models hold significant promise for enhancing the accuracy and effectiveness of emotion recognition systems. The main tool we have used in this project is Streamlit. With the appearance of Streamlit, we can create powerful web packages that leverage the power of NLP for emotion detection.