Transformers-based feedback analysis of e-commerce: a focused study on quality assessment of agriculture products
Year: 2025
Type: article
Abstract: Ensuring the quality of agricultural products in e-commerce is a significant challenge due to product variability and the absence of direct inspection before purchase. Customer reviews serve as a critical source of information, offering insights into product freshness, packaging, and overall satisfaction. This research focuses on the agricultural product domain, where quality plays a pivotal role in ensuring consumer trust and satisfaction. Harnessing the power of large language models (LLM), this study investigates the application of state-of-the-art transformer-based models for analysing customer feedback. The research utilises fine-tuned BERT and RoBERTa models to classify and predict product quality based on sentiment and contextual analysis of user reviews. The findings highlight the remarkable performance of these models, with RoBERTa achieving the highest accuracy of 99%. This study signifies the growing importance of AI and LLMs in enhancing e-commerce practices, particularly in domains like agriculture, where product quality assessment is paramount.
Author: Wenrui Xu
Institution: Guangdong Polytechnic of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4410826656