Vietnamese Complaint Detection on E-Commerce Websites

TL;DR

This paper introduces a new Vietnamese complaint detection dataset and a methodology that achieves over 92% F1-score in identifying complaints in e-commerce reviews, aiming to enhance customer feedback analysis.

Contribution

It presents the first open-domain Vietnamese complaint detection dataset and a high-accuracy detection methodology for e-commerce reviews.

Findings

Achieved 92.16% F1-score in complaint detection

Created a dataset with 5,485 annotated reviews

High inter-annotator agreement of 87%

Abstract

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.