Extracting Actionable Insights from Text Data: A Stable Topic Model Approach

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Publication History

Received: June 19, 2020
Revised: April 28, 2021; January 18, 2022; September 12, 2022
Accepted: October 27, 2022
Published Online as Articles in Advance: August 22, 2023
Published Online in Issue: September 1, 2023


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Topic models are becoming a frequently employed tool in the empirical methods repertoire of information systems and management scholars. Given textual corpora, such as consumer reviews and online discussion forums, researchers and business practitioners often use topic modeling to either explore data in an unsupervised fashion or generate variables of interest for subsequent econometric analysis. However, one important concern stems from the fact that topic models can be notorious for their instability, i.e., the generated results could be inconsistent and irreproducible at different times, even on the same dataset. Therefore, researchers might arrive at potentially unreliable results regarding the theoretical relationships that they are testing or developing. In this paper, we attempt to highlight this problem and suggest a potential approach to addressing it. First, we empirically define and evaluate the stability problem of topic models using four textual datasets. Next, to alleviate the problem and with the goal of extracting actionable insights from textual data, we propose a new method, Stable LDA, which incorporates topical word clusters into the topic model to steer the model inference toward consistent results. We show that the proposed Stable LDA approach can significantly improve model stability while maintaining or even improving the topic model quality. Further, employing two case studies related to an online knowledge community and online consumer reviews, we demonstrate that the variables generated from Stable LDA can lead to more consistent estimations in econometric analyses. We believe that our work can further enhance management scholars’ collective toolkit to analyze ever-growing textual data.

Additional Details
Author Yi Yang and Ramanath Subramanyam
Year 2023
Volume 47
Issue 3
Keywords Topic modeling, stability, Stable LDA, text analysis, empirical analysis
Page Numbers 923-954
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