Frames Beyond Words: Applying Cluster and Sentiment Analysis to News Coverage of the Nuclear Power Issue

Bjorn Burscher*, Rens Vliegenthart, Claes H. de Vreese

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

36 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)530-545
Number of pages16
JournalSocial Science Computer Review
Volume34
Issue number5
DOIs
Publication statusPublished - 1 Oct 2016
Externally publishedYes

Keywords

  • cluster analysis
  • information retrieval
  • news framing
  • sentiment analysis

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