User Intent Identification from Online Discussions Using a Joint Aspect-Action Topic Model

Abstract

Online discussions are growing as a popular, effective and reliable source of information for users because of their liveliness, flexibility and up-to-date information. Online discussions are usually developed and advanced by groups of users with various backgrounds and intents. However because of their diversities in topics and issues discussed by the users, supervised methods are not able to accurately model such dynamic conditions. In this paper, we propose a novel unsupervised generative model to derive aspect-action pairs from online discussions. The proposed method simultaneously captures and models these two features with their relationships that exist in each thread. We assume that each user post is generated by a mixture of aspect and action topics. Therefore, we design a model that captures the latent factors that incorporates the aspect types and intended actions, which describe how users develop a topic in a discussion. In order to demonstrate the effectiveness of our approach, we empirically compare our model against the state of the art methods on large-scale discussion dataset, crawled from apple discussions with over 3.3 million user posts from 340k discussion threads.