license: unknown
datasets:
- anilguven/turkish_product_reviews_sentiment
language:
- tr
metrics:
- accuracy
- f1
- recall
- precision
tags:
- turkish
- product
- review
- bert
- classification
Model Info
This model was developed/finetuned for product review task for Turkish Language. Model was finetuned via hepsiburada.com product review dataset.
- LABEL_0: negative review
- LABEL_1: positive review
Model Sources
- Dataset: https://huggingface.co/datasets/anilguven/turkish_product_reviews_sentiment
- Paper: https://ieeexplore.ieee.org/document/9559007
- Demo-Coding [optional]: https://github.com/anil1055/Turkish_Product_Review_Analysis_with_Language_Models
- Finetuned from model [optional]: https://huggingface.co/dbmdz/bert-base-turkish-cased
Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
Results
- auprc = 0.9703364794020499
- auroc = 0.9740012964967856
- eval_loss = 0.358846469963511
- fn = 193
- fp = 207
- mcc = 0.8537512867685785
- tn = 2493
- tp = 2578
- Accuracy: %92.68
Citation
BibTeX:
@INPROCEEDINGS{9559007, author={Guven, Zekeriya Anil}, booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)}, title={The Effect of BERT, ELECTRA and ALBERT Language Models on Sentiment Analysis for Turkish Product Reviews}, year={2021}, volume={}, number={}, pages={629-632}, keywords={Computer science;Sentiment analysis;Analytical models;Computational modeling;Bit error rate;Time factors;Random forests;Sentiment Analysis;Language Model;Product Review;Machine Learning;E-commerce}, doi={10.1109/UBMK52708.2021.9559007}}
APA:
Guven, Z. A. (2021, September). The effect of bert, electra and albert language models on sentiment analysis for turkish product reviews. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 629-632). IEEE.