Vietnamese Complaint Detection on E-Commerce Websites
This work addresses the need for automated complaint detection in Vietnamese e-commerce to improve product and service quality, but it is incremental as it applies existing methods to a new language-specific dataset.
The authors tackled the problem of detecting customer complaints in Vietnamese e-commerce reviews by creating a new annotated dataset (UIT-ViOCD) with 5,485 reviews and achieved an F1-score of 92.16% for complaint identification.
Customer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Open-domain Complaint Detection dataset (UIT-ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites. After the data collection phase, we proceed to the annotation task and achieve the inter-annotator agreement Am of 87%. Then, we present an extensive methodology for the research purposes and achieve 92.16% by F1-score for identifying complaints. With the results, in the future, we aim to build a system for open-domain complaint detection in E-commerce websites.