LEIA: Linguistic Embeddings for the Identification of Affect. (arXiv:2304.10973v1 [cs.CL])

The wealth of text data generated by social media has enabled new kinds of
analysis of emotions with language models. These models are often trained on
small and costly datasets of text annotations produced by readers who guess the
emotions expressed by others in social media posts. This affects the quality of
emotion identification methods due to training data size limitations and noise
in the production of labels used in model development. We present LEIA, a model
for emotion identification …

analysis annotations arxiv data dataset datasets development embeddings emotion emotions generated identification labels language language models media model development noise production quality readers small social social media text training training data wealth