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README.md
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- turkish
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- electra
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- sentiment
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- turkish
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- electra
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- sentiment
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---
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### Model Info
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This model was developed/finetuned for movie review task for the Turkish Language. This model was finetuned via the Turkish movie review dataset.
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- LABEL_0: positive review
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- LABEL_1: negative review
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **Dataset:** http://humirapps.cs.hacettepe.edu.tr/tsad.aspx
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- **Paper:** https://dl.acm.org/doi/10.1145/3557892
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- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_Sentiment_Analysis-Hotel-and-Movie-Reviews/tree/main
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- **Finetuned from model [optional]:** https://huggingface.co/dbmdz/electra-base-turkish-cased-discriminator
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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 = 0.9746603304059135
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- auroc = 0.9755918528805285
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- eval_loss = 0.3568813125744164
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- fn = 1004
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- fp = 1130
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- mcc = 0.840187235364828
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- tn = 12220
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- tp = 12346
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- Accuracy: %92.21
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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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*@article{10.1145/3557892,
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author = {Guven, Zekeriya Anil},
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title = {The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis},
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year = {2022},
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issue_date = {February 2023},
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publisher = {Association for Computing Machinery},
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address = {New York, NY, USA},
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volume = {22},
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number = {2},
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issn = {2375-4699},
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url = {https://doi.org/10.1145/3557892},
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doi = {10.1145/3557892},
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journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
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month = {dec},
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articleno = {55},
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numpages = {16},
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keywords = {Language model, sentiment analysis, social network, natural language processing, text classification, data analysis}
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}*
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**APA:**
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*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.*
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