  The reviews of customers play an essential role in online shopping. People
often refer to reviews or comments of previous customers to decide whether to
buy a new product. Catching up with this behavior, some people create untruths
and illegitimate reviews to hoax customers about the fake quality of products.
These are called spam reviews, confusing consumers on online shopping platforms
and negatively affecting online shopping behaviors. We propose the dataset
called ViSpamReviews, which has a strict annotation procedure for detecting
spam reviews on e-commerce platforms. Our dataset consists of two tasks: the
binary classification task for detecting whether a review is spam or not and
the multi-class classification task for identifying the type of spam. The
PhoBERT obtained the highest results on both tasks, 86.89% and 72.17%,
respectively, by macro average F1 score.
