Inferring employee concerns from open text survey responses

Text analysis is increasingly used across social science fields. We apply both supervised and unsupervised methods to open-text responses from a large employee survey, an underused but widely available source of organizational data. Using a design science approach informed by sensemaking theory, we examine how different analytical choices shape the extraction of employee concerns. Two findings stand out. First, employees use open-text fields to voice concrete, pragmatic issues that differ markedly from the more abstract questions asked in the closed-form questions. Second, an unsupervised Structural Topic Model performs especially well, producing coherent themes with minimal manual coding. Taken together, the results demonstrate how organizations can use open-text data to access overlooked forms of employee voice.