Datasets:
File size: 4,117 Bytes
1639995 c171399 1639995 c171399 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
---
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)](https://dl.acm.org/doi/10.1145/3406601.3406624), whereby Indonesian tweets are collected and labeled as either sarcastic or non-sarcastic. We took the [raw data](https://github.com/skhotijah/using-lstm-for-context-based-approach-of-sarcasm-detection-in-twitter/blob/main/dataset/Indonesia/imbalanced.csv), 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](https://aclanthology.org/2022.semeval-1.111/), we used a 1:3 ratio to balance sarcastic with non-sarcastic comments.
## Dataset Structure
### Data Instances
```py
{
'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
```sh
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:
<a href="https://github.com/w11wo">
<img src="https://github.com/w11wo.png" alt="GitHub Profile" style="border-radius: 50%;width: 64px;border: solid 1px #fff;margin:0 4px;">
</a>
## References
```bibtex
@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",
}
``` |