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---
license: unknown
datasets:
- anilguven/turkish_product_reviews_sentiment
language:
- tr
metrics:
- accuracy
- f1
- recall
tags:
- turkish
- product
- review
- albert
- bert
---
### 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/loodos/albert-base-turkish-uncased
-
#### 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
<!-- 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.
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