task_categories:summarization multilinguality:monolingual size_categories:n<1K language:tr license:cc-by-sa-4.0 region:us
---
language:
- tr
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
task_categories:
- summarization
pretty_name: Corona-mini
---

# Dataset Card for turkish-nlp-suite/Corona-mini

## Dataset Description
- **Repository:** [Turkish Corona-mini corpus](https://github.com/turkish-nlp-suite/Corona-mini-dataset)
- **Paper:** [ACL link]()
- **Dataset:** Corona-mini
- **Domain:** Social Media

<img src="https://raw.githubusercontent.com/turkish-nlp-suite/.github/main/profile/corona-mini.png"  width="20%" height="20%"> 

### Dataset Summary
This is a tiny Turkish corpus consisting of comments about Corona symptoms. The corpus is compiled from two Ekşisözlük headlines "covid-19 belirtileri" and "gün gün koronavirüs belirtileri": 

https://eksisozluk.com/covid-19-belirtileri--6416646  

https://eksisozluk.com/gun-gun-koronavirus-belirtileri--6757665

This corpus 

- contains 178 raw, 175 processed comments
- all comments are in Turkish
- comes in 2 versions, raw and mildly processed.


For the processed version html tags, expressions in brackets and some other tags are removed.
if you want more information about how this dataset is crafted you can watch the playlist of my campaign "Turkish NLP with Duygu": [How to compile datasets](https://www.youtube.com/playlist?list=PLJTHlIwB8Vco4ONU_mCNOYIcVyFA9QrBr).
If you want to process this dataset with spaCy Turkish you can watch: [Recipes with spaCy Turkish](https://www.youtube.com/watch?v=w0WCkgCOzzw&list=PLJTHlIwB8VcoWxYHnsZOQCxWOraW42NBj)


### Dataset Instances
An instance of this dataset looks as follows:

```
{
"text": "beni sarsmayan belirtilerdir, 2 doz biontech aşılıyım, 2. doz üzerinden 5 aydan çok geçmişti cuma : ayın 12 si akşamı açık havada az üşümeye maruz kaldım."
}
```


### Data Split

|  name   |train|
|---------|----:|
|Corona-mini|175|



### Citation
This work is supported by Google Developer Experts Program. Part of Duygu 2022 Fall-Winter collection, "Turkish NLP with Duygu"/ "Duygu'yla Türkçe NLP". All rights reserved. If you'd like to use this dataset in your own work, please kindly cite [A Diverse Set of Freely Available Linguistic Resources for Turkish](https://aclanthology.org/2023.acl-long.768/) :

```
@inproceedings{altinok-2023-diverse,
    title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish",
    author = "Altinok, Duygu",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.768",
    pages = "13739--13750",
    abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.",
}
```

