CARER: Contextualized Affect Representations for Emotion Recognition
Elvis Saravia | Hsien-Chi Toby Liu | Yen-Hao Huang | Junlin Wu | Yi-Shin Chen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.