Dialogue modelling in multi-party social media conversation

Abstract

Social Media is a rich source of human-human interactions on exhausting number of topics. Although dialogue modeling from human-human interactions is not new, but there is no previous work as far as our knowledge attempting to model dialogues from social media data. This paper implements and compares multiple supervised and unsupervised approaches for dialogue modelling from social media conversation; each approach exploiting and unfolding special properties of informal conversations in social media. A new frequency measure is proposed especially for text classification problem in these type of data.