Customer Feedback Monitoring in E-Commerce with NLP

Abstract

The focus of this paper is on applying NLP to enhance the safety monitoring of electronic products. While the online retail market is expanding, product safety plays a critical role in forming customers’ trust, preserving the company’s reputation, and handling legal concerns. Standard methods of product safety risk management are ineffective in handling such a large number of customer responses, resulting in inefficiency and potential missed risks. NLP appears to provide a possible solution to the proposed problem by analyzing large volumes of customer reviews, comments, and complaint data that most e-commerce platforms receive, allowing them to identify any emergent issues regarding product safety. As the scale of e-commerce continues to grow, monitoring the safety of products becomes increasingly tricky and remains challenging across diverse markets and in different categories of products. It demonstrates how NLP models can be applied to help manage safety feedback, develop robust models against noisy and imbalanced data, and enable multilingual analysis to accommodate global feedback. One of the significant components of the study is the analysis of how NLP can be used in the automation of product safety investigation, which makes this process faster and more accurate. Additionally, the paper looks critically at the future evolution of NLP; it highlights that AI technology and machine learning will enhance product safety monitoring systems more. NLP then promises to help e-commerce platforms identify safemitigateld trust with customers and satisfy legal requirements. The paper concludes by arguing that as NLP technology advances, there will still be more prospects for the development of innovations in product safety that can assist business entities in sustaining their competitive advantage in the sphere of e-commerce.