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- W2912153384 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." @default.
- W2912153384 created "2019-02-21" @default.
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- W2912153384 date "2019-02-14" @default.
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- W2912153384 title "Using machine learning for communication classification" @default.
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- W2912153384 doi "https://doi.org/10.1007/s10683-018-09600-z" @default.
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