Thematic content analysis using supervised machine learning: An empirical evaluation using German online news

Michael Scharkow ()

Quality & Quantity: International Journal of Methodology, 2013, vol. 47, issue 2, 773 pages

Abstract: In recent years, two approaches to automatic content analysis have been introduced in the social sciences: semantic network analysis and supervised text classification. We argue that, although less linguistically sophisticated than semantic parsing techniques, statistical machine learning offers many advantages for applied communication research. By using manually coded material for training, supervised classification seamlessly bridges the gap between traditional and automatic content analysis. In this paper, we briefly introduce the conceptual foundations of machine learning approaches to text classification and discuss their application in social science research. We then evaluate their potential in an experimental study in which German online news was coded with established thematic categories. Moreover, we investigate whether and how linguistic preprocessing can improve classification quality. Results indicate that supervised text classification is generally robust and reliable for some categories, but may even be useful when it fails. Copyright Springer Science+Business Media B.V. 2013

Keywords: Content analysis; Machine learning; Online news; Bayesian classifier (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11135-011-9545-7

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