Guilt by Association: Emotion Intensities in Lexical Representations

• URL: http://arxiv.org/abs/2104.08679v1
• Date: Sun, 18 Apr 2021 02:03:52 GMT
• Title: Guilt by Association: Emotion Intensities in Lexical Representations
• Authors: Shahab Raji, Gerard de Melo
• Abstract summary: 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. 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.
• Score: 35.416923187323945
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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