metadata

tags:
  - sentiment-analysis
language:
  - ind

indolem_sentiment

IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.

This dataset is based on binary classification (positive and negative), with distribution:

Train: 3638 sentences
Development: 399 sentences
Test: 1011 sentences

The data is sourced from 1) Twitter (Koto and Rahmaningtyas, 2017)

and 2) hotel reviews.

The experiment is based on 5-fold cross validation.

Dataset Usage

Run pip install nusacrowd before loading the dataset through HuggingFace's load_dataset.

Citation

@article{DBLP:journals/corr/abs-2011-00677,
  author    = {Fajri Koto and
               Afshin Rahimi and
               Jey Han Lau and
               Timothy Baldwin},
  title     = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
               Model for Indonesian {NLP}},
  journal   = {CoRR},
  volume    = {abs/2011.00677},
  year      = {2020},
  url       = {https://arxiv.org/abs/2011.00677},
  eprinttype = {arXiv},
  eprint    = {2011.00677},
  timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

License

Creative Commons Attribution Share-Alike 4.0 International

Homepage

https://indolem.github.io/

NusaCatalogue

For easy indexing and metadata: https://indonlp.github.io/nusa-catalogue