This is a design-science paper about methods for explaining data-driven classifications of text documents. Document classification has widespread applications, such as with web pages for advertising, emails for legal discovery, blog entries for sentiment analysis, and many more. Document data...

Traditional event models underlying naive Bayes classifiers assume probability distributions that are not appropriate for binary data generated by human behaviour. In this work, we develop a new event model, based on a somewhat forgotten distribution created by Kenneth Ted Wallenius in 1963. We...

Many document classification applications require human understanding of the reasons for data-driven classification decisions: by managers, client-facing employees, and the technical team. Predictive models treat documents as data to be classified, and document data are characterized by very...