  This paper describes a system developed for detecting propaganda techniques
from news articles. We focus on examining how emotional salience features
extracted from a news segment can help to characterize and predict the presence
of propaganda techniques. Correlation analyses surfaced interesting patterns
that, for instance, the "loaded language" and "slogan" techniques are
negatively associated with valence and joy intensity but are positively
associated with anger, fear and sadness intensity. In contrast, "flag waving"
and "appeal to fear-prejudice" have the exact opposite pattern. Through
predictive experiments, results further indicate that whereas BERT-only
features obtained F1-score of 0.548, emotion intensity features and BERT hybrid
features were able to obtain F1-score of 0.570, when a simple feedforward
network was used as the classifier in both settings. On gold test data, our
system obtained micro-averaged F1-score of 0.558 on overall detection efficacy
over fourteen propaganda techniques. It performed relatively well in detecting
"loaded language" (F1 = 0.772), "name calling and labeling" (F1 = 0.673),
"doubt" (F1 = 0.604) and "flag waving" (F1 = 0.543).
