Guilt by Association: Emotion Intensities in Lexical Representations
Computation and Language
2021-04-20 v1
Abstract
What do word vector representations reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from word vector representations. Overall, we find that word vectors carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than achieved by state-of-the-art emotion lexicons.
Keywords
Cite
@article{arxiv.2104.08679,
title = {Guilt by Association: Emotion Intensities in Lexical Representations},
author = {Shahab Raji and Gerard de Melo},
journal= {arXiv preprint arXiv:2104.08679},
year = {2021}
}