Text Based Emotion Detection

Text-based emotion detection aims to automatically identify and categorize emotions expressed in written text, enabling more empathetic human-computer interaction and insightful analysis of large text corpora. Current research heavily utilizes transformer-based models like BERT and RoBERTa, often enhanced with techniques like ordinal classification to better capture the nuances of emotional expression and address class imbalances, and sometimes incorporating additional features such as emojis or psycholinguistic information to improve accuracy and generalizability. This field is significant for its applications in diverse areas like customer service, mental health monitoring, and bias detection in AI systems, with ongoing efforts focused on mitigating biases inherent in training data and improving the robustness of models across different text domains.