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---
datasets:
- maydogan/Turkish_SentimentAnalysis_TRSAv1
language:
- tr
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- google-bert/bert-base-multilingual-cased
pipeline_tag: text-classification
tags:
- Turkish Sentiment Analysis
---
# 🇹🇷 BERTurk for Turkish Sentiment Analysis
This model is a fine-tuned version of [mBERT](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the [TRSAv1 dataset](https://huggingface.co/maydogan/Turkish_SentimentAnalysis_TRSAv1), 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](https://dergipark.org.tr/en/pub/ejt/issue/92270/1592448).
---
## How to Use
You can use the model directly with 🤗 Transformers:
```python
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)
```
---
## 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}}}
}
```
---
## Dataset Overview
The [TRSAv1 dataset](https://huggingface.co/maydogan/Turkish_SentimentAnalysis_TRSAv1) 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 |
---
## 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 |
---
## Acknowledgments
Special thanks to [maydogan](https://huggingface.co/maydogan) for their contributions and support.
--- |