Multi-Label Emotion Tagging For Online News By Supervised Topic Model
Published 2015 · Computer Science
An enormous online news services provide users with interactive platforms where users can freely share their subjective emotions, such as sadness, surprise, and anger, towards the news articles. Such emotions can not only help understand the preferences and perspectives of individual users, but also benefit a number of online applications to provide users with more relevant services. While most of previous approaches are intended for recognizing a single emotion of the author, it has been observed that different emotions of the readers are more representative of the news articles. Therefore, this paper focuses on predicting readers’ multiple emotions evoked by online news. To the best of our knowledge, this is the first research work for addressing the task. This paper proposes a novel supervised topic model which introduces an additional emotion layer to associate latent topics with evoked multiple emotions of readers. In particular, the model generates a set of latent topics from emotions, followed by generating words from each topic. The experiments on the real dataset from online news service demonstrate the effectiveness of the proposed approach in multi-label emotion tagging for online news.