Transformer-Based Bidirectional Encoder Representations for Emotion Detection from Text

Social media influences internet users to share their sentiments, feelings, or emotions about entities. In particular, sentiment analysis classifies a text into positive, negative, or neutral. It does not capture the state of mind of an individual like happiness, anger, and fear. Therefore, emotion detection plays an important role in user-generated content for capturing the state of mind. Moreover, researchers adopted traditional machine learning and deep learning models to capture emotions from the text. Recently, transformers-based architectures achieve better results in various natural language processing tasks. Therefore, we propose a transformer-based emotion detection system, which uses context-dependent features and a one-cycle learning rate policy for a better understanding of emotions from the text. We evaluate the proposed emotion detection model using error matrix, learning curve, precision, recall, F1-score, and their micro and macro averages. Our results indicate that the system achieves a 6 % accuracy over existing models.