Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58%). We also extend the task beyond emotion types to model Robert Plutchik's 8 primary emotion dimensions, acquiring a superior accuracy of 95.68%.
REF used distant supervision to construct a large dataset from the general Twitter for fine-grained emotion detection and explored deep learning models to detect emotions.
EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks
ACL
Computer Science