Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Sub-tasks:
sentiment-classification
Languages:
Turkish
Size:
100K - 1M
License:
File size: 1,166 Bytes
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---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
languages:
- tr-TR
licenses:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Turkish Sentiment Dataset
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset
This dataset contains positive , negative and notr sentences from several data sources given in the references. In the most sentiment models , there are only two labels; positive and negative. However , user input can be totally notr sentence. For such cases there were no data I could find. Therefore I created this dataset with 3 class. Positive and negative sentences are listed below. Notr examples are extraced from turkish wiki dump. In addition, added some random text inputs like "Lorem ipsum dolor sit amet.".
# References
- https://www.kaggle.com/burhanbilenn/duygu-analizi-icin-urun-yorumlari
- https://github.com/fthbrmnby/turkish-text-data
- https://www.kaggle.com/mustfkeskin/turkish-wikipedia-dump
- https://github.com/ezgisubasi/turkish-tweets-sentiment-analysis
- http://humirapps.cs.hacettepe.edu.tr/ |