Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text
Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text
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DOI：
10.18653/v1/2021.findings-acl.358
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Kunwoo Park;Zhufeng Pan;Jungseock Joo
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文献类型：
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作者：
Kunwoo Park;Zhufeng Pan;Jungseock Joo
理解新闻文本中谁责备或支持谁是计算社会科学中的一个关键研究问题。然而，传统的情感分析方法和数据集不适合于政治文本领域，因为它们没有考虑实体之间表达的情感的方向。在本文中，我们提出了一种新的NLP任务，即从给定的新闻文档中识别政治实体之间的定向情感关系，我们称之为定向情感抽取。从百万规模的新闻语料库中，我们构建了一个人工标注政治实体情感关系的新闻句子数据集。我们提出了一种简单但有效的方法来利用预先训练的转换器，该方法通过预测多个问答任务并结合结果来推断目标类。我们通过分析两个重大事件：2016年美国总统大选和新冠肺炎中政治实体之间的正面和负面观点，展示了我们提出的方法在社会科学研究问题中的实用性。新提出的问题、数据和方法将促进未来对跨学科自然语言处理方法和应用的研究。©2021计算语言学协会
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. © 2021 Association for Computational Linguistics