Cross-Category Defect Discovery from Online Reviews: Supplementing Sentiment with Category-Specific Semantics

A semiautomated risk assessment method for consumer products

“…Several prior studies have conducted a comparative analysis of the smoke term scoring methodology against commonly used machine learning approaches such as deep learning and sentiment analysis in the context of detecting sparsely populated target categories. The findings of these studies demonstrate that the smoke term scoring methodology often outperforms or is on par with these "black box" approaches, including deep learning word embedding models, in various domains (Brahma et al, 2021;Zaman et al, 2021). Moreover, Goldberg, Gruss et al (2022) noted that the primary advantage of the smoke term scoring approach over the black box methods is its interpretability, as the latter often lack in this regard, which could limit the adoption of the black box approaches by practitioners.…”

“…For example, online consumers repeatedly post their negative comments on product usage, but most of these negative comments are complaints about the product (e.g., value and effectiveness), and few of these negative comments are safety hazards. A series of past studies concentrated on applying text-classification methods to defect discovery and safety surveillance (Abrahams et al, 2015;Adams et al, 2017;Law et al, 2017;Mummalaneni et al, 2018;Nasri et al, 2018;Winkler et al, 2016;Zaman et al, 2021). Nasri et al (2018) investigated online videos of customers who used multiple product categories to detect the existence of a safety hazard in general.…”

“…Hence, these 740 hazard-tagged reviews were finalized to analyze the risk level of each product in the remainder of this study for the specific categorization of different hazard types. According to prior studies' suggestions (Zaman et al, 2020(Zaman et al, , 2021, we focused on sentence-level tagging instead of review-level tagging. This study disentangles the hazard-tagged reviews by manually tagging each sentence of these reviews for revealing specific discussions of hazard categories across a diverse set of 10 product categories.…”

“…We ran a machine learning algorithm, which uses the prevalence measure or correlation coefficient (CC) score, developed by Fan et al (2005). Past studies, such as Abrahams et al (2012,2013,2015), Adams et al (2017), and Zaman et al (2021), discussed the smoke term methodology.…”

“…In December 2016, a Pew Research Center survey found that 79 percent of US adults purchased a product directly through online media. Customers post product reviews on retailer websites (e.g., Amazon.com and Walmart.com) and discussion forums (e.g., Patient.info and Toyotanation.com) for sharing their complaints with other customers regarding product defects (Abrahams et al., 2012; Zaman et al., 2021; Zhang et al., 2019) and safety hazards (Nasri et al., 2018).…”