Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Sub-tasks:
sentiment-classification
Languages:
Turkish
Size:
100K - 1M
License:
| 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/ |