What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
This work addresses the challenge of analyzing both content and style in microblog conversations, which is incremental as it builds on existing topic modeling and discourse analysis methods.
The paper tackles the problem of jointly modeling topics and discourse in microblog conversations using an unsupervised neural framework, resulting in coherent topics and meaningful discourse behavior that improve message classification when jointly trained.
This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse). Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier.