license: unknown
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
tags:
- movie
- review
- turkish
- bert
- sentiment
Model Info
This model was developed/finetuned for movie review task for the Turkish Language. This model was finetuned via the Turkish movie review dataset.
- LABEL_0: positive review
- LABEL_1: negative review
Model Sources
- 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/loodos/albert-base-turkish-uncased
Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
Results
- Accuracy: %91.71
Citation
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.