  As the e-commerce market continues to expand and online transactions
proliferate, customer reviews have emerged as a critical element in shaping the
purchasing decisions of prospective buyers. Previous studies have endeavored to
identify key aspects of customer reviews through the development of sentiment
analysis models and topic models. However, extracting specific dissatisfaction
factors remains a challenging task. In this study, we delineate the pain point
detection problem and propose Painsight, an unsupervised framework for
automatically extracting distinct dissatisfaction factors from customer reviews
without relying on ground truth labels. Painsight employs pre-trained language
models to construct sentiment analysis and topic models, leveraging attribution
scores derived from model gradients to extract dissatisfaction factors. Upon
application of the proposed methodology to customer review data spanning five
product categories, we successfully identified and categorized dissatisfaction
factors within each group, as well as isolated factors for each type. Notably,
Painsight outperformed benchmark methods, achieving substantial performance
enhancements and exceptional results in human evaluations.
