Using machine learning for communication classification
研究用机器学习对实验中的通信内容自动分类，用人工编码数据集训练模型，发现计算机分类能较好匹配人工分类，有望降低研究成本并实现大规模数据分析。
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.