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.

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Dataset used to train anilguven/albert_tr_turkish_product_reviews

Collection including anilguven/albert_tr_turkish_product_reviews