  Understanding who blames or supports whom in news text is a critical research
question in computational social science. Traditional methods and datasets for
sentiment analysis are, however, not suitable for the domain of political text
as they do not consider the direction of sentiments expressed between entities.
In this paper, we propose a novel NLP task of identifying directed sentiment
relationship between political entities from a given news document, which we
call directed sentiment extraction. From a million-scale news corpus, we
construct a dataset of news sentences where sentiment relations of political
entities are manually annotated. We present a simple but effective approach for
utilizing a pretrained transformer, which infers the target class by predicting
multiple question-answering tasks and combining the outcomes. We demonstrate
the utility of our proposed method for social science research questions by
analyzing positive and negative opinions between political entities in two
major events: 2016 U.S. presidential election and COVID-19. The newly proposed
problem, data, and method will facilitate future studies on interdisciplinary
NLP methods and applications.
