  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.
