Metadata Integration for Spam Reviews Detection on Vietnamese E-commerce Websites

TL;DR

This paper introduces the ViSpamReviews v2 dataset and a novel integration of textual and categorical metadata for improved spam review detection on Vietnamese e-commerce sites, achieving state-of-the-art results.

Contribution

It presents a new dataset and a combined deep learning approach that leverages metadata and text features for more accurate spam review classification.

Findings

Product category enhances DNN performance.

Text features improve detection accuracy.

Achieved state-of-the-art results with PhoBERT and SPhoBert.

Abstract

The problem of detecting spam reviews (opinions) has received significant attention in recent years, especially with the rapid development of e-commerce. Spam reviews are often classified based on comment content, but in some cases, it is insufficient for models to accurately determine the review label. In this work, we introduce the ViSpamReviews v2 dataset, which includes metadata of reviews with the objective of integrating supplementary attributes for spam review classification. We propose a novel approach to simultaneously integrate both textual and categorical attributes into the classification model. In our experiments, the product category proved effective when combined with deep neural network (DNN) models, while text features performed well on both DNN models and the model achieved state-of-the-art performance in the problem of detecting spam reviews on Vietnamese e-commerce websites, namely PhoBERT. Specifically, the PhoBERT model achieves the highest accuracy when combined with product description features generated from the SPhoBert model, which is the combination of PhoBERT and SentenceBERT. Using the macro-averaged F1 score, the task of classifying spam reviews achieved 87.22% (an increase of 1.64% compared to the baseline), while the task of identifying the type of spam reviews achieved an accuracy of 73.49% (an increase of 1.93% compared to the baseline).