This model is a fine-tuned version of XLM-RoBERTa 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:
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 (%)
83.27
83.30
83.27
83.22
Class-wise Performance
Sentiment
Precision (%)
Recall (%)
F1 Score (%)
Negative
88.67
83.99
86.27
Neutral
76.97
75.43
76.19
Positive
84.25
90.38
87.21
Acknowledgments
Special thanks to maydogan for their contributions and support.
