SEACrowd/unisent

UniSent is a universal sentiment lexica for 1000+ languages. To build UniSent, the authors use a massively parallel Bible corpus to project sentiment information from English to other languages for sentiment analysis on Twitter data. 173 of 1404 languages are spoken in Southeast Asia

Languages

aaz, abx, ace, acn, agn, agt, ahk, akb, alj, alp, amk, aoz, atb, atd, att, ban, bbc, bcl, bgr, bgs, bgz, bhp, bkd, bku, blw, blz, bnj, bpr, bps, bru, btd, bth, bto, bts, btx, bug, bvz, bzi, cbk, ceb, cfm, cgc, clu, cmo, cnh, cnw, csy, ctd, czt, dgc, dtp, due, duo, ebk, fil, gbi, gdg, gor, heg, hil, hlt, hnj, hnn, hvn, iba, ifa, ifb, ifk, ifu, ify, ilo, ind, iry, isd, itv, ium, ivb, ivv, jav, jra, kac, khm, kix, kje, kmk, kne, kqe, krj, ksc, ksw, kxm, lao, lbk, lew, lex, lhi, lhu, ljp, lsi, lus, mad, mak, mbb, mbd, mbf, mbi, mbs, mbt, mej, mkn, mmn, mnb, mnx, mog, mqj, mqy, mrw, msb, msk, msm, mta, mtg, mtj, mvp, mwq, mwv, mya, nbe, nfa, nia, nij, nlc, npy, obo, pag, pam, plw, pmf, pne, ppk, prf, prk, pse, ptu, pww, sas, sbl, sda, sgb, smk, sml, sun, sxn, szb, tbl, tby, tcz, tdt, tgl, tha, tih, tlb, twu, urk, vie, war, whk, wrs, xbr, yli, yva, zom, zyp

Supported Tasks

Sentiment Analysis

Dataset Usage

Using datasets library

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

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("unisent", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("unisent"))
# 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/ehsanasgari/UniSent

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Creative Commons Attribution Non Commercial No Derivatives 4.0 (cc-by-nc-nd-4.0)

Citation

If you are using the Unisent dataloader in your work, please cite the following:

@inproceedings{asgari2020unisent,
title={UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages},
author={Asgari, Ehsaneddin and Braune, Fabienne and Ringlstetter, Christoph and Mofrad, Mohammad RK},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC-2020)},
year={2020},
organization={European Language Resources Association (ELRA)}
}


@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}
}