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 reviews are called spam reviews, which confuse consumers on online shopping platforms and negatively affect 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 a spam or not and the multi-class classification task for identifying the type of spam. The PhoBERT obtained the highest results on both tasks, 88.93% and 72.17%, respectively, by macro average F1 score.
翻译：对客户的审查在网上购物中发挥着必不可少的作用。 人们经常引用前客户的审查或评论来决定是否购买新产品。 赶上这一行为,一些人制造了虚假产品质量的不真实和非法的审查给骗局客户。 这些审查被称为垃圾邮件审查,混淆了在线购物平台上的消费者,对在线购物行为产生了负面影响。 我们提议了称为Vispam审查的数据集,该数据集有一个严格的批注程序,用于检测电子商务平台上的垃圾审查。 我们的数据集包括两项任务: 检测审查是否是垃圾邮件的二元分类任务和识别垃圾邮件类型的多级分类任务。 PhoBERT通过宏观平均F1分,分别获得了88.93%和72.17%这两个任务的最高结果。
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