Guilt by Association: Emotion Intensities in Lexical Representations
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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2021
- DOI:
- 10.48550/arXiv.2104.08679
- arXiv:
- arXiv:2104.08679
- Bibcode:
- 2021arXiv210408679R
- Keywords:
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- Computer Science - Computation and Language