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README.md
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- review
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- bert
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- classification
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- review
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- bert
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- classification
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---
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### Model Info
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This model was developed/finetuned for product review task for Turkish Language. Model was finetuned via hepsiburada.com product review dataset.
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- LABEL_0: negative review
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- LABEL_1: positive review
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_product_reviews_sentiment
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- **Paper:** https://ieeexplore.ieee.org/document/9559007
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- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Product_Review_Analysis_with_Language_Models
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- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/bert-base-turkish-cased
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#### Preprocessing
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You must apply removing stopwords, stemming, or lemmatization process for Turkish.
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### Results
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- auprc = 0.9703364794020499
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- auroc = 0.9740012964967856
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- eval_loss = 0.358846469963511
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- fn = 193
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- fp = 207
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- mcc = 0.8537512867685785
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- tn = 2493
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- tp = 2578
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- Accuracy: %92.68
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## Citation
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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@INPROCEEDINGS{9559007,
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author={Guven, Zekeriya Anil},
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booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)},
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title={The Effect of BERT, ELECTRA and ALBERT Language Models on Sentiment Analysis for Turkish Product Reviews},
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year={2021},
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volume={},
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number={},
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pages={629-632},
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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},
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doi={10.1109/UBMK52708.2021.9559007}}
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**APA:**
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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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