Interpreting Topic Models Using Prototypical Text: From ‘Telling’ To ‘Showing

Abstract

While topic models are increasingly used for natural language processing in management and strategy research, their interpretability remains challenging. If researchers restrict the number of topics for simplicity, the induced topic structure may not be statistically accurate. If they do not, they face difficulties in interpreting topics in a transparent and reproducible way. We propose the “prototypicaltext based interpretation” (PTBI) of topic models, a methodology that gives a rule-based approach for selecting text from the corpus to interpret topic structures. PTBI enables transparent and replicable topic interpretation, a move from “telling” to “showing” pivotal for qualitative research. We illustrate PTBI by studying the organizational culture of Netflix, based on text reviews employees post on Glassdoor.com. We compare our findings to the company’s own public account of its culture and show how PTBI improves the state of the art for topic models interpretation by documenting how our approach differs from and improves on prior practice.