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Model Info

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.

  • LABEL_0: angry
  • LABEL_1: afraid
  • LABEL_2: happy
  • LABEL_3: surprised
  • LABEL_4: sad

Model Sources


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


  • eval_loss = 0.06813859832385788
  • mcc = 0.9843707754295762
  • Accuracy: %98.75



@INPROCEEDINGS{9559014, author={Guven, Zekeriya Anil}, booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)}, title={Comparison of BERT Models and Machine Learning Methods for Sentiment Analysis on Turkish Tweets}, year={2021}, volume={}, number={}, pages={98-101}, 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.}, doi={10.1109/UBMK52708.2021.9559014}}


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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Dataset used to train anilguven/bert_tr_turkish_tweet