Using machine learning for communication classification

Penczynski, Stefan P. (2019) Using machine learning for communication classification. Experimental Economics, 22 (4). 1002–1029. ISSN 1386-4157

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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.

Item Type: Article
Uncontrolled Keywords: classification,communication,machine learning,economics, econometrics and finance (miscellaneous) ,/dk/atira/pure/subjectarea/asjc/2000/2001
Faculty \ School: Faculty of Social Sciences > School of Economics
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 11 Dec 2018 11:30
Last Modified: 06 Aug 2020 23:46
URI: https://ueaeprints.uea.ac.uk/id/eprint/69263
DOI: 10.1007/s10683-018-09600-z

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