Toward Automatic Understanding of the Function of Affective Language in Support Groups

TL;DR

This paper emphasizes the importance of understanding the social and communicative functions of affective language in support forums, proposing that incorporating these elements enhances automated emotion recognition beyond simple sentiment analysis.

Contribution

It advocates for integrating communicative and social functions into NLP models for better understanding of affective language in support groups, supported by experiments on a sentiment-labelled medical forum corpus.

Findings

Fine-grained analysis improves emotion detection accuracy.

Recognizing social functions enhances understanding of affective expressions.

Experiments demonstrate the value of incorporating communicative elements.

Abstract

Understanding expressions of emotions in support forums has considerable value and NLP methods are key to automating this. Many approaches understandably use subjective categories which are more fine-grained than a straightforward polarity-based spectrum. However, the definition of such categories is non-trivial and, in fact, we argue for a need to incorporate communicative elements even beyond subjectivity. To support our position, we report experiments on a sentiment-labelled corpus of posts taken from a medical support forum. We argue that not only is a more fine-grained approach to text analysis important, but simultaneously recognising the social function behind affective expressions enable a more accurate and valuable level of understanding.