This article introduces a process for computational text classification that can be used in a variety of qualitative research and evaluation settings. The process leverages supervised machine learning based on an implementation of a multinomial Bayesian classifier. Applied to a community of inquiry framework, the algorithm was used to identify evidence of cognitive presence, social presence, and teaching presence in the text contributions (44,000 unique posts) of more than 4,000 participants in an online environmental education course. Results indicate that computational text classification can significantly reduce labor costs and can help Extension research faculty scale, accelerate, and ensure reproducibility of their research.
Smith, J. G., & Tissing, R. (2018). Using Computational Text Classification for Qualitative Research and Evaluation in Extension. Journal of Extension, 56(2), Article 4. https://tigerprints.clemson.edu/joe/vol56/iss2/4