Identifying Disputed Topics in the News
Identifying Disputed Topics in the News
复制标题
识别新闻中有争议的话题
DOI：
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发表时间：
2014
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影响因子：
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通讯作者：
Heiko Paulheim
中科院分区：
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作者：
Orphée De Clercq;S. Hertling;Veronique Hoste;Simone Paolo Ponzetto;Heiko Paulheim
新闻文章经常反映一种观点或观点，某些话题比其他话题更能引起不同的意见。为了分析和更好地理解公共语篇，识别这些有争议的话题是一个有趣的研究问题。在本文中，我们描述了一种结合自然语言处理技术和DBpedia的背景知识来发现新闻网站中有争议的话题的方法。为了识别这些主题，我们用DBpedia概念注释每一篇文章，提取它们的类别，并计算情感分数，以便识别那些揭示不同媒体之间极性显著偏差的类别。我们通过对六个受欢迎的英国和美国新闻网站的样本进行定性评估来说明我们的方法。
News articles often reflect an opinion or point of view, with certain topics evoking more diverse opinions than others. For analyzing and better understanding public discourses, identifying such contested topics constitutes an interesting research question. In this paper, we describe an approach that combines NLP techniques and background knowledge from DBpedia for finding disputed topics in news sites. To identify these topics, we annotate each article with DBpedia concepts, extract their categories, and compute a sentiment score in order to identify those categories revealing significant deviations in polarity across different media. We illustrate our approach in a qualitative evaluation on a sample of six popular British and American news sites.