transformers pytorch bert text-classification turkish hotel review sentiment tr license:unknown autotrain_compatible endpoints_compatible region:us
---
language:
- tr
license: unknown
metrics:
- accuracy
- f1
- precision
- recall
tags:
- turkish
- hotel
- review
- sentiment
- bert
---
### Model Info

This model was developed/finetuned for hotel review task for the Turkish Language. This model was finetuned via the Turkish hotel review dataset. 
- LABEL_0: positive review
- LABEL_1: negative review

### Model Sources

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

- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
- **Paper:** https://dl.acm.org/doi/10.1145/3557892
- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/bert-base-turkish-uncased

#### Preprocessing 

You must apply removing stopwords, stemming, or lemmatization process for Turkish.

### Results

- auprc = 0.9955237027430079
- auroc = 0.9931789536266349
- eval_loss = 0.16509666319230998
- fn = 107
- fp = 17
- mcc = 0.9577026908774097
- tn = 2883
- tp = 2793
- Accuracy: %97.86

## 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:**

*@article{10.1145/3557892,
author = {Guven, Zekeriya Anil},
title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3557892},
doi = {10.1145/3557892},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {dec},
articleno = {55},
numpages = {16},
keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
}*

**APA:**

*Guven, Z. A. (2022). The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-16.*