metadata
language:
- ind
pretty_name: Emot
task_categories:
- emotion-classification
tags:
- emotion-classification
EmoT is an emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happiness, love, and sadness. EmoT dataset is splitted into 3 sets with 3521 train, 440 validation, 442 test data.
Languages
ind
Supported Tasks
Emotion Classification
Dataset Usage
Using datasets library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/emot", trust_remote_code=True)
Using seacrowd library
# Load the dataset using the default config
dset = sc.load_dataset("emot", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("emot"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
More details on how to load the seacrowd library can be found here.
Dataset Homepage
https://github.com/IndoNLP/indonlu
Dataset Version
Source: 1.0.0. SEACrowd: 2024.06.20.
Dataset License
Creative Commons Attribution Share-Alike 4.0 International
Citation
If you are using the Emot dataloader in your work, please cite the following:
@inproceedings{saputri2018emotion,
title={Emotion classification on indonesian twitter dataset},
author={Saputri, Mei Silviana and Mahendra, Rahmad and Adriani, Mirna},
booktitle={2018 International Conference on Asian Language Processing (IALP)},
pages={90--95},
year={2018},
organization={IEEE}
}
@inproceedings{wilie2020indonlu,
title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
author={Wilie, Bryan and Vincentio, Karissa and Winata, Genta Indra and Cahyawijaya, Samuel and Li, Xiaohong and Lim, Zhi Yuan and Soleman, Sidik and Mahendra, Rahmad and Fung, Pascale and Bahar, Syafri and others},
booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
pages={843--857},
year={2020}
}
@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}
}