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.

本文介绍了Painsight，一种无监督的框架，通过使用预训练的语言模型和构建情感分析和主题模型，利用模型梯度导出的归因分数自动从顾客评价中提取出不同的不满因素，并成功地应用于五种产品类别的客户评价数据中，提出的方法在人工评估中表现出卓越的结果。

基于在线客户评论的疼点检测的可扩展观点挖掘框架