We propose a graph-based mechanism to extract rich-emotion bearing patterns, which fosters a deeper analysis of online emotional expressions, from a corpus. The patterns are then enriched with word embeddings and evaluated through several emotion recognition tasks. Moreover, we conduct analysis on the emotion-oriented patterns to demonstrate its applicability and to explore its properties. Our experimental results demonstrate that the proposed techniques outperform most state-of-the-art emotion recognition techniques.
@article{arxiv.1804.08847,
title = {DeepEmo: Learning and Enriching Pattern-Based Emotion Representations},
author = {Elvis Saravia and Hsien-Chi Toby Liu and Yi-Shin Chen},
journal= {arXiv preprint arXiv:1804.08847},
year = {2018}
}