Emotion classification is a research field that aims to detect the emotions in a text using machine learning methods. In traditional machine learning (TML) methods, feature engineering processes cause the loss of some meaningful information, and classification performance is negatively affected. In addition, the success of modelling using deep learning (DL) approaches depends on the sample size. More samples are needed for Turkish due to the unique characteristics of the language. However, emotion classification data sets 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 TML and DL methods in experimental studies. The proposed approach provides state-of-the-art performance for Turkish emotion classification.

情感分类是一个研究领域，旨在使用机器学习方法检测文本中的情感。在传统的机器学习（TML）方法中，特征工程过程会导致一些有意义的信息丢失，并且分类性能受到负面影响。此外，使用深度学习（DL）方法进行建模的成功取决于样本量。由于土耳其语的独特特征，因此需要更多样本。但是，土耳其语中的情绪分类数据集非常有限。在这项研究中，使用了预训练的语言模型方法来创建更强大的土耳其语情感分类模型。为此，对众所周知的预训练语言模型进行了微调。在实验研究中，将这些微调的土耳其情感分类模型的性能与TML和DL方法的性能进行了全面比较。所提出的方法为土耳其情绪分类提供了最先进的性能。