language:ind license:mit emotion-classification region:us
EmotCMT is an emotion classification Indonesian-English code-mixing dataset created through an Indonesian-English code-mixed Twitter data pipeline consisting of 4 processing steps, i.e., tokenization, language identification, lexical normalization, and translation. The dataset consists of 825 tweets, 22.736 tokens with 11.204 Indonesian tokens and 5.613 English tokens. Each tweet is labelled with an emotion, i.e., cinta (love), takut (fear), sedih (sadness), senang (joy), or marah (anger).
@inproceedings{barik-etal-2019-normalization,
    title = "Normalization of {I}ndonesian-{E}nglish Code-Mixed {T}witter Data",
    author = "Barik, Anab Maulana  and
      Mahendra, Rahmad  and
      Adriani, Mirna",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5554",
    doi = "10.18653/v1/D19-5554",
    pages = "417--424"
}

@article{Yulianti2021NormalisationOI,
  title={Normalisation of Indonesian-English Code-Mixed Text and its Effect on Emotion Classification},
  author={Evi Yulianti and Ajmal Kurnia and Mirna Adriani and Yoppy Setyo Duto},
  journal={International Journal of Advanced Computer Science and Applications},
  year={2021}
}
---
license: mit
tags:
- emotion-classification
language:
- ind
---

# emotcmt

EmotCMT is an emotion classification Indonesian-English code-mixing dataset created through an Indonesian-English code-mixed Twitter data pipeline consisting of 4 processing steps, i.e., tokenization, language identification, lexical normalization, and translation. The dataset consists of 825 tweets, 22.736 tokens with 11.204 Indonesian tokens and 5.613 English tokens. Each tweet is labelled with an emotion, i.e., cinta (love), takut (fear), sedih (sadness), senang (joy), or marah (anger).

## Dataset Usage

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

## Citation

```
@inproceedings{barik-etal-2019-normalization,
    title = "Normalization of {I}ndonesian-{E}nglish Code-Mixed {T}witter Data",
    author = "Barik, Anab Maulana  and
      Mahendra, Rahmad  and
      Adriani, Mirna",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5554",
    doi = "10.18653/v1/D19-5554",
    pages = "417--424"
}

@article{Yulianti2021NormalisationOI,
  title={Normalisation of Indonesian-English Code-Mixed Text and its Effect on Emotion Classification},
  author={Evi Yulianti and Ajmal Kurnia and Mirna Adriani and Yoppy Setyo Duto},
  journal={International Journal of Advanced Computer Science and Applications},
  year={2021}
}
```

## License

MIT

## Homepage

[https://github.com/ir-nlp-csui/emotcmt](https://github.com/ir-nlp-csui/emotcmt)

### NusaCatalogue

For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)