metadata

datasets:
  - maydogan/Turkish_SentimentAnalysis_TRSAv1
language:
  - tr
metrics:
  - accuracy
  - precision
  - recall
  - f1
base_model:
  - dbmdz/bert-base-turkish-128k-cased
pipeline_tag: text-classification
tags:
  - Turkish Sentiment Analysis

🇹🇷 BERTurk for Turkish Sentiment Analysis

This model is a fine-tuned version of BERTurk 128k 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.

How to Use

You can use the model directly with 🤗 Transformers:

from transformers import pipeline

classifier = pipeline("text-classification", model="incidelen/bert-base-turkish-sentiment-analysis-128k-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 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 (%)
83.68	83.69	83.68	83.66

Class-wise Performance

Sentiment	Precision (%)	Recall (%)	F1 Score (%)
Negative	88.35	85.20	86.74
Neutral	77.01	76.45	76.73
Positive	85.70	89.38	87.50

Acknowledgments

Special thanks to maydogan for their contributions and support.