There exist two main approaches to automatically extract affective
orientation: lexicon-based and corpus-based. In this work, we argue that these
two methods are compatible and show that combining them can improve the
accuracy of emotion classifiers...
In particular, we introduce a novel variant of
the Label Propagation algorithm that is tailored to distributed word
representations, we apply batch gradient descent to accelerate the optimization
of label propagation and to make the optimization feasible for large graphs,
and we propose a reproducible method for emotion lexicon expansion. We conclude
that label propagation can expand an emotion lexicon in a meaningful way and
that the expanded emotion lexicon can be leveraged to improve the accuracy of
an emotion classifier.