Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews (2023)

Abstract: 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.

## introduction the thriving e-commerce market has rendered online customer reviews an indispensable factor in influencing the purchasing decisions of potential consumers @xcite @xcite . these reviews offer invaluable insights for businesses, empowering them to refine their products and services @xcite . the analysis of copious customer reviews to comprehend customer needs and pain points is of paramount importance for augmenting service quality and heightening customer satisfaction @xcite @xcite . pain points pertain to specific difficulties or problems encountered by customers while utilizing a product or service @xcite . these encompass emotional challenges arising from psychological demands and the incongruities between customers' actual perceptions and their expectations of products and services. identifying and addressing pain points serves as a crucial initial step in enhancing the quality of products and services @xcite @xcite . the increasing importance of customer reviews has spurred a wealth of research into comprehending their effects through the application of natural language processing methodologies, encompassing opinion mining, sentiment analysis, topic modeling, and keyword extraction @xcite @xcite . nonetheless, the identification of specific attributes, such as pain points in customer review data, necessitates annotation tailored to each product and service. owing to this constraint, prior studies have predominantly concentrated on devising methods for extracting keywords, a notion more expansive than pain points. @xcite extracted attributes from product specifications, while @xcite employed a bert-based bio tagging @xcite model to extract aspect and opinion terms. more recently, researchers have shifted their focus toward aspectbased sentiment analysis @xcite , striving to analyze customers' opinions at the granular aspect level, as opposed to mere classification into being positive or negative @xcite . moreover, @xcite utilized latent dirichlet allocation (lda) to differentiate review types and explore topic-related negative emotions. existing research has mainly focused on extracting keywords and aspect terms from reviews or simply analyzing review types; nonetheless, such methodologies exhibit limitations. firstly, pain points are not only more specific but also contingent upon the product or service in question, in contrast to keywords or aspect terms. this is because customers may articulate disparate pain points utilizing identical keywords. consequently, a comprehensive definition of pain points must be established prior to their detection @xcite @xcite . secondly, even when equipped with a definition for pain points, the unique nature of pain points across products and services necessitates the ongoing annotation of new products, an endeavor that is both labor-intensive and costly @xcite . to overcome these limitations, an automated framework for the definition, extraction, and analysis of pain points from customer reviews is requisite, which is applicable to any product or service. in this study, we propose painsight, an extendable opinion-mining framework for pain point detection, composed of a series of modules. painsight implements a pipeline that conducts sentiment analysis and topic modeling using pre-trained language models, subsequently extracting pain points based on gradient-based attribution. when applied to customer reviews encompassing five categories of home appliances, painsight effectively classified pain points emerging in diverse product groups. the extracted pain points exhibited substantial enhancements in performance, both quantitatively and qualitatively, in comparison to the results procured by the baseline model. the main contributions of this study can be summarized as follows: ## problem statement we aim to detect pain points in user-generated content (ugc), such as customer reviews, to identify customer discomforts and challenges @xcite . however, the absence of standardized criteria for determining what constitutes pain points, contingent on the target product or service, poses a challenge because pain points exhibit variation in scale @xcite . as a result, the precise definition of pain points pertinent to the target product or service is crucial. ## painsight the primary objective of the proposed framework is to model the entire process of automatically detecting pain points in real-world scenarios. specifically, this study aims to address the following two practical research questions: considering the significance of both perspectives from prior works, a framework capable of generating accurate and diverse output, covering a wide range of distinct pain points, is necessary. the architecture of painsight, depicted in figure 1 , features a parallel structure to incorporate these two research questions. ## -(a) ## conclusion in this study, we propose painsight, a novel framework for automatically extracting and evaluating pain points from customer reviews. we address the under-explored problem of pain point detection and present a practical pipeline for real-world scenarios. by employing sentiment analysis and topic modeling, we identify sentiment-aware and topic-aware pain points that reflect customer perceptions and various types of discomfort. the final output is obtained by extracting the most important words or features from the data using a gradient-based attribution score. this score enables us to determine which words or features are most critical in influencing the model's decision-making process and utilize this information in the post-process to recognize more accurate and meaningful pain points. experimental results demonstrate that painsight outperforms existing models on five product group reviews, with human evaluation results indicating a high level of agreement compared to the baseline. future work could involve incorporating diverse customer feedback and constructing a high-quality benchmark dataset to further validate and enhance the proposed approach.