incidelen/bert-base-multilingual-turkish-sentiment-analysis-cased

🇹🇷 BERTurk for Turkish Sentiment Analysis

This model is a fine-tuned version of mBERT on the TRSAv1 dataset, a labeled collection of Turkish e-commerce reviews categorized into positive, neutral, and negative sentiments. For more details about the dataset, methodology, and experiments, you can refer to the corresponding research paper.

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How to Use

You can use the model directly with 🤗 Transformers:

from transformers import pipeline

classifier = pipeline("text-classification", model="incidelen/bert-base-multilingual-turkish-sentiment-analysis-cased")
result = classifier("Ürün çok kaliteli, paketleme harikaydı. Kesinlikle tavsiye ederim!")
print(result)

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Citation

If you use this model in your research or application, please cite the following paper:

@article{incidelen15sentiment,
  title={Sentiment Analysis in Turkish Using Language Models: A Comparative Study},
  author={{\.I}ncidelen, Mert and Aydo{\u{g}}an, Murat},
  journal={European Journal of Technique (EJT)},
  volume={15},
  number={1},
  pages={68--74},
  publisher={Hibetullah KILI{\c{C}}}
}

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Dataset Overview

The TRSAv1 dataset includes 150,000 Turkish product reviews from e-commerce platforms. It is balanced across three sentiment classes:

Sentiment	Count
Negative	50,000
Neutral	50,000
Positive	50,000
TOTAL	150,000

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Evaluation Results

Overall Performance

Accuracy (%)	Precision (%)	Recall (%)	F1 Score (%)
81.86	82.00	81.86	81.89

Class-wise Performance

Sentiment	Precision (%)	Recall (%)	F1 Score (%)
Negative	87.79	82.36	84.99
Neutral	74.20	75.69	74.94
Positive	84.03	87.54	85.75

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Acknowledgments

Special thanks to maydogan for their contributions and support.

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