Using machine learning for communication classification

Stefan P. Penczynski ()
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Stefan P. Penczynski: University of East Anglia

Experimental Economics, 2019, vol. 22, issue 4, No 10, 1002-1029

Abstract: Abstract The present study explores the value of machine learning techniques in the classification of communication content in experiments. Previously human-coded datasets are used to both train and test algorithm-generated models that relate word counts to categories. For various games, the computer models of the classification are able to match out-of-sample the human classification to a considerable extent. The analysis raises hope that the substantial effort going into such studies can be reduced by using computer algorithms for classification. This would enable a quick and replicable analysis of large-scale datasets at reasonable costs and widen the applicability of such approaches. The paper gives an easily accessible technical introduction into the computational method.

Keywords: Communication; Classification; Machine learning (search for similar items in EconPapers)
JEL-codes: C63 C91 D83 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (19)

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DOI: 10.1007/s10683-018-09600-z

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