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A study of Turkish emotion classification with pretrained language models
- Publication Year :
- 2022
-
Abstract
- © The Author(s) 2021.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.
- Subjects :
- Feature engineering
Turkish
Computer science
business.industry
Emotion classification
020206 networking & telecommunications
02 engineering and technology
Library and Information Sciences
computer.software_genre
Field (computer science)
language.human_language
0202 electrical engineering, electronic engineering, information engineering
language
020201 artificial intelligence & image processing
Artificial intelligence
Language model
business
computer
Natural language processing
Information Systems
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....d0919df8402526253e93e4a1a3bbf8e0