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Abstract:
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Reviews from online markets are critical for companies to obtain feedback and develop strategies. One of the most common analysis approaches of the unstructured online reviews is to follow a two-stage procedure, where one first derives text features through topic modeling techniques and subsequently estimates linear models for prediction and inference. Yet, there is lack of a guidance in choosing different topic modeling techniques for forecasting in various text scenarios. In this paper, we perform the first ever benchmark study on both simulated texts and real online reviews to provide guidance for practitioners in selecting topic modeling methods depending on properties of the textual corpus. We argue that when the research design combines estimated topics as independent variables within linear models, as is common in applied economic and decision analyses, the best topic modeling method should balance capturing the underlying textual themes in addition to maximizing statistical and forecasting power of the regression model.
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