SEACrowd/uit_vihsd

Tasks

• sentiment-analysis

Citation text from the card

@InProceedings{10.1007/978-3-030-79457-6_35,
author="Luu, Son T.
and Nguyen, Kiet Van
and Nguyen, Ngan Luu-Thuy",
editor="Fujita, Hamido
and Selamat, Ali
and Lin, Jerry Chun-Wei
and Ali, Moonis",
title="A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts",
booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="415--426",
abstract="In recent years, Vietnam witnesses the mass development of social network users on different social
platforms such as Facebook, Youtube, Instagram, and Tiktok. On social media, hate speech has become a critical
problem for social network users. To solve this problem, we introduce the ViHSD - a human-annotated dataset for
automatically detecting hate speech on the social network. This dataset contains over 30,000 comments, each comment
in the dataset has one of three labels: CLEAN, OFFENSIVE, or HATE. Besides, we introduce the data creation process
for annotating and evaluating the quality of the dataset. Finally, we evaluate the dataset by deep learning and transformer models.",
isbn="978-3-030-79457-6"
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah an

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