  Most previous research on moral frames has focused on social media short
texts, little work has explored moral sentiment within news articles. In news
articles, authors often express their opinions or political stance through
moral judgment towards events, specifically whether the event is right or wrong
according to social moral rules. This paper initiates a new task to understand
moral opinions towards events in news articles. We have created a new dataset,
EMONA, and annotated event-level moral opinions in news articles. This dataset
consists of 400 news articles containing over 10k sentences and 45k events,
among which 9,613 events received moral foundation labels. Extracting event
morality is a challenging task, as moral judgment towards events can be very
implicit. Baseline models were built for event moral identification and
classification. In addition, we also conduct extrinsic evaluations to integrate
event-level moral opinions into three downstream tasks. The statistical
analysis and experiments show that moral opinions of events can serve as
informative features for identifying ideological bias or subjective events.
