  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).
