  Social scientists often classify text documents to use the resulting labels
as an outcome or a predictor in empirical research. Automated text
classification has become a standard tool, since it requires less human coding.
However, scholars still need many human-labeled documents to train automated
classifiers. To reduce labeling costs, we propose a new algorithm for text
classification that combines a probabilistic model with active learning. The
probabilistic model uses both labeled and unlabeled data, and active learning
concentrates labeling efforts on difficult documents to classify. Our
validation study shows that the classification performance of our algorithm is
comparable to state-of-the-art methods at a fraction of the computational cost.
Moreover, we replicate two recently published articles and reach the same
substantive conclusions with only a small proportion of the original labeled
data used in those studies. We provide activeText, an open-source software to
implement our method.
