---
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

<!-- Provide the basic links for the model. -->

- **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

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**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.