Abstract:

The collaborative effort of theory-driven content analysis can benefit
significantly from the use of topic analysis methods, which allow researchers
to add more categories while developing or testing a theory. This additive
approach enables the reuse of previous efforts of analysis or even the
merging of separate research projects, thereby making these methods
more accessible and increasing the discipline’s ability to create and share
content analysis capabilities. This paper proposes a weakly supervised topic
analysis method that uses both a low-cost unsupervised method to compile
a training set and supervised deep learning as an additive and accurate
text classification method. We test the validity of the method, specifically
its additivity, by comparing the results of the method after adding 200
categories to an initial number of 450. We show that the suggested method
provides a foundation for a low-cost solution for large-scale topic analysis.

Publisher's Version