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Content analysis is a common and flexible technique to quantify and make sense of qualitative data in psychological research. However, the practical implementation of content analysis is extremely labor-intensive and subject to human coder errors. Applying natural language processing (NLP) techniques can help address these limitations. We explain and illustrate these techniques to psychological researchers. For this purpose, we first present a study exploring the creation of psychometrically meaningful predictions of human content codes. Using an existing database of human content codes, we build an NLP algorithm to validly predict those codes, at generally acceptable standards. We then conduct a Monte-Carlo simulation to model how four dataset characteristics (ie, sample size, unlabeled proportion of cases, classification base rate, and human coder reliability) influence content classification performance …