Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Sub-tasks:
sentiment-classification
Languages:
Turkish
Size:
100K - 1M
License:
metadata
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.".
There are 492.782 labeled sentences. %10 of them used for testing.