CARER: Contextualized Affect Representations for Emotion Recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, Yi-Shin Chen · 2018
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 semisupervised, 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.