Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
Javanese
Size:
100K - 1M
License:
metadata
annotations_creators:
- found
extended:
- original
language_creators:
- machine-generated
languages:
- jv
licenses:
- odbl-1.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
Dataset Card for "imdb-javanese"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: Github
- Repository: Github
- Paper: Aclweb
- Point of Contact: Wilson Wongso
- Size of downloaded dataset files: 17.0 MB
- Size of the generated dataset: 47.5 MB
- Total amount of disk used: 64.5 MB
Dataset Summary
Large Movie Review Dataset translated to Javanese. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. We translated the original IMDB Dataset to Javanese using the multi-lingual MarianMT Transformer model from Helsinki-NLP/opus-mt-en-mul.
Supported Tasks and Leaderboards
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
An example of javanese_imdb_train.csv looks as follows.
| label | text |
|---|---|
| 1 | "Drama romantik sing digawé karo direktur Martin Ritt kuwi ora dingertèni, nanging ana momen-momen sing marahi karisma lintang Jane Fonda lan Robert De Niro (kelompok sing luar biasa). Dhèwèké dadi randha sing ora isa mlaku, iso anu anyar lan anyar-inventor-- kowé isa nganggep isiné. Adapsi novel Pat Barker ""Union Street"" (yak titel sing apik!) arep dinggo-back-back it on bland, lan pendidikan film kuwi gampang, nanging isih nyenengké; a rosy-hued-inventor-fantasi. Ora ana sing ngganggu gambar sing sejati ding kok iso dinggo nggawe gambar sing paling nyeneng." |
| 0 | "Pengalaman wong lanang sing nduwé perasaan sing ora lumrah kanggo babi. Mulai nganggo tuladha sing luar biasa yaiku komedia. Wong orkestra termel digawé dadi wong gila, sing kasar merga nyanyian nyanyi. Sayangé, kuwi tetep absurd wektu WHOLE tanpa ceramah umum sing mung digawé. Malah, sing ana ing jaman kuwi kudu ditinggalké. Diyalog kryptik sing nggawé Shakespeare marah gampang kanggo kelas telu. Pak teknis kuwi luwih apik timbang kowe mikir nganggo cinematografi sing apik sing jenengé Vilmos Zsmond. Masa depan bintang Saly Kirkland lan Frederic Forrest isa ndelok." |
Data Fields
text: The movie review translated into Javanese.label: The sentiment exhibited in the review, either1(positive) or0(negative).
Data Splits Sample Size
| train | unsupervised | test |
|---|---|---|
| 25000 | 50000 | 25000 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}