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.

本文介绍了一项新的 NLP 任务，名为 directed sentiment extraction，可用于计算社会科学中分析政治性新闻文本中谁责备或支持谁的情感方向。该方法利用预训练变压器神经网络进行目标分类预测，并结合多种问题回答任务的结果。通过分析美国总统选举和 COVID-19 等两个事件中政治实体之间的正面和负面意见，证明了该方法的实用性，可促进未来 NLP 方法和应用的跨学科研究。