Text Mining Based FMEA on Online Reviews With POStagging2vec

Abstract

Failure Modes and Effects Analysis (FMEA) is a systematic methodology employed to identify, prioritize, and mitigate potential and existing failure modes within a system. Despite its efficacy, the manual acquisition and processing of failure data from extensive datasets is a resource‐intensive endeavor. In the digital era, vast quantities of consumer‐generated data regarding product performance are available on e‐commerce platforms, social media, and specialized forums. However, the sheer volume and unstructured nature of this “Big Data” render manual interpretation and classification practically unfeasible. Consequently, sophisticated text mining techniques are required to extract actionable intelligence from these massive review corpora. While the literature extensively discusses quality improvement via data mining, research integrating advanced text mining within the FMEA framework remains scarce. This gap is addressed by proposing POStagging2vec, a novel text mining methodology designed to extract the most representative failure‐defining sentences without the need for exhaustive manual review. POStagging2vec enhances the analytical process by measuring semantic similarity between review sentences and cluster labels augmented with salient parts of speech, specifically adverbs, adjectives, verbs, and compounds. To validate the proposed framework, a case study was conducted on Amazon customer reviews for a robot vacuum cleaner. From an initial dataset comprising 820 reviews and 5906 sentences, the methodology efficiently isolated the 140 most relevant sentences, leading to the identification and evaluation of 20 critical failure modes. The results demonstrate that this integrated approach allows organizations to rapidly detect significant failures and expedite the implementation of corrective actions, thereby enhancing product reliability and consumer satisfaction via a more effective and successful R&D process for their products.