README.md

---
datasets:
- maydogan/Turkish_SentimentAnalysis_TRSAv1
language:
- tr
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- dbmdz/electra-base-turkish-cased-discriminator
pipeline_tag: text-classification
tags:
- Turkish Sentiment Analysis
---
# 🇹🇷 ELECTRA-TR for Turkish Sentiment Analysis
This model is a fine-tuned version of [ELECTRA Base Turkish](https://huggingface.co/dbmdz/electra-base-turkish-cased-discriminator) 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/electra-base-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 (%)      |
|-----------------|------------------|-----------------|-------------------|
| 83.64           | 83.64            | 83.64           | 83.58             |
### Class-wise Performance
| Sentiment       | Precision (%)    | Recall (%)      | F1 Score (%)      |
|-----------------|------------------|-----------------|-------------------|
| Negative        | 88.84            | 84.60           | 86.67             |
| Neutral         | 77.53            | 75.41           | 76.46             |
| Positive        | 84.55            | 90.90           | 87.61             |

---
## Acknowledgments
Special thanks to [maydogan](https://huggingface.co/maydogan) for their contributions and support.

---