SEACrowd/code_mixed_jv_id

Sentiment analysis and machine translation data for Javanese and Indonesian.

Languages

jav, ind

Supported Tasks

Sentiment Analysis, Machine Translation

Dataset Usage

Using datasets library

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

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("code_mixed_jv_id", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("code_mixed_jv_id"))
# 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://iopscience.iop.org/article/10.1088/1742-6596/1869/1/012084

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

cc_by_3.0

Citation

If you are using the Code Mixed Jv Id dataloader in your work, please cite the following:

@article{Tho_2021,
  doi = {10.1088/1742-6596/1869/1/012084},
  url = {https://doi.org/10.1088/1742-6596/1869/1/012084},
  year = 2021,
  month = {apr},
  publisher = {{IOP} Publishing},
  volume = {1869},
  number = {1},
  pages = {012084},
  author = {C Tho and Y Heryadi and L Lukas and A Wibowo},
  title = {Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach},
  journal = {Journal of Physics: Conference Series},
  abstract = {Nowadays mixing one language with another language either in
  spoken or written communication has become a common practice for bilingual
  speakers in daily conversation as well as in social media. Lexicon based
  approach is one of the approaches in extracting the sentiment analysis. This
  study is aimed to compare two lexicon models which are SentiNetWord and VADER
  in extracting the polarity of the code-mixed sentences in Indonesian language
  and Javanese language. 3,963 tweets were gathered from two accounts that
  provide code-mixed tweets. Pre-processing such as removing duplicates,
  translating to English, filter special characters, transform lower case and
  filter stop words were conducted on the tweets. Positive and negative word
  score from lexicon model was then calculated using simple mathematic formula
  in order to classify the polarity. By comparing with the manual labelling,
  the result showed that SentiNetWord perform better than VADER in negative
  sentiments. However, both of the lexicon model did not perform well in
  neutral and positive sentiments. On overall performance, VADER showed better
  performance than SentiNetWord. This study showed that the reason for the
  misclassified was that most of Indonesian language and Javanese language
  consist of words that were considered as positive in both Lexicon model.}
}


@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}
}