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
Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
Results
- auprc = 0.9588538437395457
- auroc = 0.9653234951018236
- eval_loss = 0.37227460598843365
- fn = 188
- fp = 288
- mcc = 0.826593937301856
- tn = 2479
- tp = 2516
- Accuracy: %91.30
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.
