Vietnamese Complaint Detection on E-Commerce Websites
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese
NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers
New Benchmark Dataset and Fine-Grained Cross-Modal Fusion Framework for Vietnamese Multimodal Aspect-Category Sentiment Analysis
PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
A benchmark for toxic comment classification on Civil Comments dataset
토론
이 논문과 연결된 포럼 글이 아직 없습니다.