Affective Event Classification with Discourse-enhanced Self-training

Prior research has recognized the need to associate affective polarities with events and has produced several techniques and lexical resources for identifying affective events. Our research introduces new classification models to assign affective polarity to event phrases. First, we present a BERT-based model for affective event classification and show that the classifier achieves substantially better performance than a large affective event knowledge base. Second, we present a discourse-enhanced selftraining method that iteratively improves the classifier with unlabeled data. The key idea is to exploit event phrases that occur with a coreferent sentiment expression. The discourseenhanced self-training algorithm iteratively labels new event phrases based on both the classifier's predictions and the polarities of the event's coreferent sentiment expressions. Our results show that discourse-enhanced selftraining further improves both recall and precision for affective event classification.