SEACrowd/id_hsd_nofaaulia

Id Hsd Nofaaulia数据集是一个用于检测印尼语长文档中仇恨言论的数据集。该数据集是从Facebook上构建的，旨在通过机器学习方法，特别是支持向量机（SVM）作为分类算法，来检测社交媒体上的仇恨言论。研究表明，使用TF-IDF、字符四元组、单词一元组和词典特征，SVM在检测仇恨言论方面取得了85%的F1分数。该数据集支持情感分析任务，并提供了多种加载方式，包括使用`datasets`库和`seacrowd`库。

原始信息汇总

数据集概述

语言

• 印度尼西亚语 (ind)

支持任务

• 情感分析 (Sentiment Analysis)

数据集使用

使用 datasets 库

python from datasets import load_dataset dset = datasets.load_dataset("SEACrowd/id_hsd_nofaaulia", trust_remote_code=True)

使用 seacrowd 库

python import seacrowd as sc

加载数据集使用默认配置

dset = sc.load_dataset("id_hsd_nofaaulia", schema="seacrowd")

检查数据集的所有可用子集（配置名称）

print(sc.available_config_names("id_hsd_nofaaulia"))

使用特定配置加载数据集

dset = sc.load_dataset_by_config_name(config_name="<config_name>")

数据集主页

• 数据集主页

数据集版本

• 源版本: 1.0.0
• SEACrowd版本: 2024.06.20

数据集许可证

• 未知

引用

@inproceedings{10.1145/3330482.3330491, author = {Aulia, Nofa and Budi, Indra}, title = {Hate Speech Detection on Indonesian Long Text Documents Using Machine Learning Approach}, year = {2019}, isbn = {9781450361064}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3330482.3330491}, doi = {10.1145/3330482.3330491}, abstract = {Due to the growth of hate speech on social media in recent years, it is important to understand this issue. An automatic hate speech detection system is needed to help to counter this problem. There have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the possibility of the message of the text may be hidden. In this research, we explore in detecting hate speech on Indonesian long documents using machine learning approach. We build a new Indonesian hate speech dataset from Facebook. The experiment showed that the best performance obtained by Support Vector Machine (SVM) as its classifier algorithm using TF-IDF, char quad-gram, word unigram, and lexicon features that yield f1-score of 85%.}, booktitle = {Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence}, pages = {164–169}, numpages = {6}, keywords = {machine learning, SVM, long documents, hate speech detection}, location = {Bali, Indonesia}, series = {ICCAI 19} }

@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 and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, year={2024}, eprint={2406.10118}, journal={arXiv preprint arXiv: 2406.10118} }