Toward Automatic Understanding of the Function of Affective Language in Support Groups
This work addresses the need for more accurate emotion analysis in support groups, but it is incremental as it builds on existing fine-grained approaches.
The paper tackled the problem of understanding emotions in support forums by arguing for incorporating communicative elements beyond subjectivity, and reported experiments on a medical support forum corpus to show that recognizing social functions improves accuracy and value.
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.