Using machine learning for communication classification

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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.
Original languageEnglish
Pages (from-to)1002–1029
Number of pages28
JournalExperimental Economics
Volume22
Issue number4
Early online date14 Feb 2019
DOIs
Publication statusPublished - Dec 2019

Keywords

  • Classification
  • Communication
  • Machine learning

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