This repository includes training scripts, the finetuned model, and the dataset for BOUN Turkish Sentiment Analysis.

Implementation

You can use the finetuned model with the HuggingFace library. See the model link for more details and demo.

from transformers import pipeline
bounti = pipeline("sentiment-analysis",model="akoksal/bounti")
print(bounti("Bu yemeği pek sevmedim"))
>> [{'label': 'negative', 'score': 0.8012508153915405}]

Dataset

You can find the dataset in the data folder with the training, validation, and test splits.

Due to Twitter copyright, we cannot release the full text of the tweets. We share the tweet IDs, and the full text can be downloaded throught official Twitter API.

	Training	Validation	Test
Positive	1691	188	469
Neutral	3034	338	843
Negative	1008	113	280
Total	5733	639	1592

Model

BERTurk model: Download (1.3 gb)

The scores of the finetuned model with BERTurk:

	Accuracy	Precision	Recall	F1
Validation	0.745	0.706	0.730	0.715
Test	0.723	0.692	0.729	0.701

Citation

You can cite the following paper if you use our work:

@INPROCEEDINGS{BounTi,
  author={Köksal, Abdullatif and Özgür, Arzucan},
  booktitle={2021 29th Signal Processing and Communications Applications Conference (SIU)}, 
  title={Twitter Dataset and Evaluation of Transformers for Turkish Sentiment Analysis}, 
  year={2021},
  volume={},
  number={}
  }

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