Text Mining in Survey Data

Abstract

Free text responses in surveys contain important information and should be analyzed by researchers. However, human coding of survey text is not only expensive, but also vulnerable to subjectivity. An automated text mining approach can solve these problems. Therefore, we demonstrate using the supervised latent Dirichlet allocation (sLDA) to jointly analyze text and numerical data in an employee satisfaction survey. For each rating, the algorithm outputs selected words as the “topic” and estimates the credible interval. Finally, we discuss future applications and advantages of utilizing survey text.