Frames Beyond Words: Applying Cluster and Sentiment Analysis to News Coverage of the Nuclear Power Issue
Frames Beyond Words: Applying Cluster and Sentiment Analysis to News Coverage of the Nuclear Power Issue
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DOI：
10.1177/0894439315596385
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发表时间：
2016-10-01
影响因子：
4.1
通讯作者：
de Vreese, Claes H.
中科院分区：
文献类型：
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作者：
Burscher, Bjorn;Vliegenthart, Rens;de Vreese, Claes H.
自动分析媒体内容的方法正在显著进步。其中，通过统计方法分析新闻文章的框架结构已变得越来越普遍。在本文中，我们研究了k-均值聚类分析和自动情感分析相结合的新闻框架的概念有效性。此外，我们测试了一种改进统计框架分析的方法，以便所显示的文章簇更接近地反映框架概念。为此，我们只使用文章标题和引言中的单词，并从分析中排除命名实体和具有特定词性的单词。为了验证显示的框架，我们手动分析提取的聚类中的文章样本。我们的测试结果表明，当遵循所提出的特征选择方法时，所得到的聚类更准确地区分具有不同边框的文章。我们讨论了我们的发现的方法论和理论含义。
Methods to automatically analyze media content are advancing significantly. Among others, it has become increasingly popular to analyze the framing of news articles by means of statistical procedures. In this article, we investigate the conceptual validity of news frames that are inferred by a combination of k-means cluster analysis and automatic sentiment analysis. Furthermore, we test a way of improving statistical frame analysis such that revealed clusters of articles reflect the framing concept more closely. We do so by only using words from an article's title and lead and by excluding named entities and words with a certain part of speech from the analysis. To validate revealed frames, we manually analyze samples of articles from the extracted clusters. Findings of our tests indicate that when following the proposed feature selection approach, the resulting clusters more accurately discriminate between articles with a different framing. We discuss the methodological and theoretical implications of our findings.