# Vietnamese Complaint Detection on E-Commerce Websites

arXiv: https://arxiv.org/abs/2104.11969 · HTML: https://mlsanity.com/paper/2104.11969/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 24 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [VnCoreNLP: A Vietnamese Natural Language Processing Toolkit](https://mlsanity.com/paper/1801.01331/) (2018)
- [Automatically Identifying Complaints in Social Media](https://mlsanity.com/paper/1906.03890/) (2019)
- [A Large-scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2103.11528/) (2021)
- [Emotion Recognition for Vietnamese Social Media Text](https://mlsanity.com/paper/1911.09339/) (2019)
- [Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese](https://mlsanity.com/paper/2103.10069/) (2021)
- [ReINTEL: A Multimodal Data Challenge for Responsible Information Identification on Social Network Sites](https://mlsanity.com/paper/2012.08895/) (2020)

## Built on by (most cited first, 4 of 4)

- [SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?](https://mlsanity.com/paper/2508.12243/) (2025)
- [Span Detection for Aspect-Based Sentiment Analysis in Vietnamese](https://mlsanity.com/paper/2110.07833/) (2021)
- [ViTHSD: Exploiting Hatred by Targets for Hate Speech Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2404.19252/) (2024)
- [ViGoEmotions: A Benchmark Dataset For Fine-grained Emotion Detection on Vietnamese Texts](https://mlsanity.com/paper/2602.08371/) (2026)

## Abstract (arXiv)

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.