Representing Affect Information in Word Embeddings. (arXiv:2209.10583v1 [cs.CL])

A growing body of research in natural language processing (NLP) and natural
language understanding (NLU) is investigating human-like knowledge learned or
encoded in the word embeddings from large language models. This is a step
towards understanding what knowledge language models capture that resembles
human understanding of language and communication. Here, we investigated
whether and how the affect meaning of a word (i.e., valence, arousal,
dominance) is encoded in word embeddings pre-trained in large neural networks.
We used the human-labeled dataset …

arxiv information word embeddings