Abstract
Using automated classifiers to code discourse data enables researchers to carry out analyses on large datasets. This paper presents a detailed example of applying training, validation and test sets frequently utilized in machine learning to develop automated classifiers for use in quantitative ethnography research. The method was applied to two dispositional constructs. Within one cycle of the process, reliable and valid automated classifiers were developed for Social Disposition. However, the automated coding scheme for Inclusive Disposition was rejected during the validation stage due to issues of overfitting. Nonetheless, the results demonstrate the beneficial potential of using preclassified datasets in enhancing the efficiency and effectiveness of the automation process.
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The authors gratefully acknowledge funding support from the US National Science Foundation for the work this paper reports. Views appearing in this paper do not reflect those of the funding agency.
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Lee, S.B., Gui, X., Manquen, M., Hamilton, E.R. (2019). Use of Training, Validation, and Test Sets for Developing Automated Classifiers in Quantitative Ethnography. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds) Advances in Quantitative Ethnography. ICQE 2019. Communications in Computer and Information Science, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_10
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