A Topic-based Segmentation Model for Identifying Segment-Level Drivers of Star Ratings from Unstructured Text Reviews

However, analyzing this data is challenging due to the unstructured nature of text. This paper introduces a novel machine-learning method for identifying interpretable key drivers of star ratings from text reviews, which might vary across segments.

By adopting the Ising model prior to account for dependence between words, the model simultaneously achieves segmentation, identifies segment-level key topics (i.e., groups of frequently co-occurring words), and estimates the impacts of the selected words on the ratings. We first demonstrate that the proposed model successfully identifies segment-specific key drivers of customer satisfaction using illustrative simulated review data.