Emotion recognition from text is a crucial task in Natural Language Processing (NLP) that aims to detect and classify emotions expressed in written language. This project focuses on developing an advanced deep learning model to classify emotions in textual data accurately. Given the growing use of sentiment analysis in areas such as social media monitoring, customer feedback analysis, and mental health applications, building an efficient emotion recognition system is highly relevant.
The project will utilize state-of-the-art transformer-based models, such as BERT and RoBERTa, to capture contextual meaning and improve classification accuracy. The proposed system will preprocess textual data, tokenize it, and use fine-tuned pre-trained language models to identify and classify emotions. The expected outcome is a robust emotion recognition model that can outperform traditional lexicon-based and classical machine learning approaches.