SEACrowd/vispamreviews

Tasks

• sentiment-analysis

Citation text from the card

@InProceedings{10.1007/978-3-031-21743-2_48,
author="Van Dinh, Co
and Luu, Son T.
and Nguyen, Anh Gia-Tuan",
editor="Nguyen, Ngoc Thanh
and Tran, Tien Khoa
and Tukayev, Ualsher
and Hong, Tzung-Pei
and Trawi{'{n}}ski, Bogdan
and Szczerbicki, Edward",
title="Detecting Spam Reviews on Vietnamese E-Commerce Websites",
booktitle="Intelligent Information and Database Systems",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="595--607",
abstract="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.",
isbn="978-3-031-21743-2"
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite fo

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