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
Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
Results
- eval_loss = 0.5407382257189601
- mcc = 0.7682691555667568
- Accuracy: %81.37
Citation
BibTeX:
@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}}
APA:
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.
