Emotion classification is a research field that aims to detect the emotions in a text using machine learning methods. In traditional machine learning methods, feature engineering processes cause the loss of some meaningful information, and classification performance is negatively affected. Additionally, the success of modeling using deep learning approaches depends on the sample size. More samples are needed for Turkish due to the unique characteristics of the language. However, emotion classification datasets in Turkish are quite limited. In this study, the pretrained language model approach was used to create a stronger emotion classification model for Turkish. Well-known pretrained language models were fine-tuned for this purpose. The performances of these fine-tuned models for Turkish emotion classification were comprehensively compared with the performances of traditional machine learning and deep learning methods in experimental studies. The proposed approach provides state-of-the-art performance for Turkish emotion classification.
Recommended citation: Uçan A., Dörterler M., Sezer E. A., (2021) A Study of Turkish Emotion Classification with Pretrained Language Models, Journal of Information Science