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README.md
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- tweet
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- emotion
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- sentiment
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- tweet
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- emotion
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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 tweet emotion detection task for the Turkish Language. This model was finetuned via tweet dataset. This dataset contains 5 classes: angry, happy, sad, surprised and afraid.
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- LABEL_0: angry
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- LABEL_1: afraid
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- LABEL_2: happy
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- LABEL_3: surprised
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- LABEL_4: sad
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **Dataset:** https://huggingface.co/datasets/anilguven/turkish_tweet_emotion_dataset
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- **Paper:** https://ieeexplore.ieee.org/document/9559014
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- **Demo-Coding [optional]:** https://github.com/anil1055/Turkish_tweet_emotion_analysis_with_language_models
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- **Finetuned from model [optional]:** https://huggingface.co/bert-base-multilingual-uncased
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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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- eval_loss = 0.5407382257189601
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- mcc = 0.7682691555667568
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- Accuracy: %81.37
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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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*@INPROCEEDINGS{9559014,
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author={Guven, Zekeriya Anil},
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booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)},
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title={Comparison of BERT Models and Machine Learning Methods for Sentiment Analysis on Turkish Tweets},
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year={2021},
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volume={},
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number={},
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pages={98-101},
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keywords={Computer science;Sentiment analysis;Analytical models;Social networking (online);Computational modeling;Bit error rate;Random forests;Sentiment Analysis;BERT;Machine Learning;Text Classification;Tweet Analysis.},
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doi={10.1109/UBMK52708.2021.9559014}}*
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**APA:**
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*Guven, Z. A. (2021, September). Comparison of BERT models and machine learning methods for sentiment analysis on Turkish tweets. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 98-101). IEEE.*
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