metadata

license: apache-2.0
language:
  - ind
pretty_name: Twitter Indonesia Sarcastic

Twitter Indonesia Sarcastic

Twitter Indonesia Sarcastic is a dataset intended for sarcasm detection in the Indonesian language. This dataset is introduced in Khotijah et al. (2020), whereby Indonesian tweets are collected and labeled as either sarcastic or non-sarcastic. We took the raw data, and performed several cleaning procedures such as: sentence order re-reversal, deduplication with minHash LSH, PII masking to remove usernames, hashtags, emails, URLs, and finally a random sampling to limit the non-sarcastic comments. Following SemEval-2022 Task 6: iSarcasmEval, we used a 1:3 ratio to balance sarcastic with non-sarcastic comments.

Dataset Structure

Data Instances

{
    'tweet': 'Terima kasih bapak <username> telah mengendalikan banjir dengan baik sehingga Jakarta saat ini tidak ada lagi yang tidak banjir.. Semua sudah merata.. ?????? <hashtag>',
    'label': 1
}

Data Fields

tweet: PII-masked Twitter tweet content.
label: 0 for non-sarcastic, 1 for sarcastic.

Data Splits

Split	#sarcastic	#non sarcastic	#total
`train`	470	1408	1878
`test`	134	404	538
`validation`	67	201	268
Total (cleaned; balanced)	671	2013	2684
Total (cleaned; unbalanced)	671	12190	12861
Total (raw)	4350	13368	17718

Dataset Directory

twitter_indonesia_sarcastic
├── README.md
├── data    # re-balanced dataset
│   ├── test.csv
│   ├── train.csv
│   └── validation.csv
└── raw_data
    ├── khotijah.csv    # raw dataset
    └── khotijah_cleaned.csv    # cleaned dataset

Authors

Twitter Indonesia Sarcastic is prepared by:

References

@inproceedings{10.1145/3406601.3406624,
    author = {Khotijah, Siti and Tirtawangsa, Jimmy and Suryani, Arie A.},
    title = {Using LSTM for Context Based Approach of Sarcasm Detection in Twitter},
    year = {2020},
    isbn = {9781450377591},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3406601.3406624},
    doi = {10.1145/3406601.3406624},
    booktitle = {Proceedings of the 11th International Conference on Advances in Information Technology},
    articleno = {19},
    numpages = {7},
    keywords = {context, Sarcasm detection, paragraph2vec, lstm, deep learning},
    location = {, Bangkok, Thailand, },
    series = {IAIT '20}
}

@inproceedings{abu-farha-etal-2022-semeval,
    title = "{S}em{E}val-2022 Task 6: i{S}arcasm{E}val, Intended Sarcasm Detection in {E}nglish and {A}rabic",
    author = "Abu Farha, Ibrahim  and
      Oprea, Silviu Vlad  and
      Wilson, Steven  and
      Magdy, Walid",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.111",
    doi = "10.18653/v1/2022.semeval-1.111",
    pages = "802--814",
}