Joint Modeling of News Reader's and Comment Writer's Emotions

Huanhuan Liu, Shoushan Li, Guodong Zhou, Chu-Ren Huang, Peifeng Li

Abstract: Emotion classification can be generally done from both the writer’s and reader’s perspectives. In this study, we find that two foundational tasks in emotion classification, i.e., reader’s emotion classification on the news and writer’s emotion classification on the comments, are strongly related to each other in terms of coarse-grained emotion categories, i.e., negative and positive. On the basis, we propose a respective way to jointly model these two tasks. In particular, a cotraining algorithm is proposed to improve semi-supervised learning of the two tasks. Experimental evaluation shows the effectiveness of our joint modeling approach.