Emotion Detection with Pre-Trained Language Models BERT and ELECTRA Analysis of Turkish Data

DOI: https://doi.org/10.58190/imiens.2024.82

Keywords

BERT, ELECTRA, Language Models, Transformer, Turkish Sentiment Analysis

Abstract

Developments in artificial intelligence have led to positive developments in many fields. Sentiment analysis, one of these areas, has become more applicable with the models and architectures developed. In this study, emotion detection and emotion analysis were performed on the transcribed data of Turkish voice recordings. In the emotion detection phase, after the emotional states (positive, negative, neutral) of the data were detected with BERT and ELECTRA models, which are transformer-based structures, machine learning algorithms were used in the accuracy analysis of these emotional states and the Google Colaboratory platform was used in the application phase. Naive Bayes, Random Forest, Support Vector Machine and Logistic Regression algorithms were used in the accuracy analysis. As a result of the study, both Naive Bayes and Logistic Regression algorithms achieved the best accuracy rate in emotion detection with the BERT model with a rate of 70%. In emotion detection with the ELECTRA model, both Random Forest and Logistic Regression algorithms achieved the best accuracy rate of 72%. BERT and ELECTRA methods are used to provide a better understanding of understanding and classification of emotional content in Turkish texts and contribute to the development of sentiment analysis-based applications. In addition, two Turkish emotion data sets were obtained, and by using more than one method in emotion analysis, our study has been a unique study in the field, allowing the analysis of the study to be done more effectively.

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    • Published: 2024-03-27

    Issue: Vol. 3 No. 1 (2024) (view)

    Section: Research Articles

    How to cite:
    [1]
    A. TEPECIK and E. DEMIR, “Emotion Detection with Pre-Trained Language Models BERT and ELECTRA Analysis of Turkish Data”, Intell Methods Eng Sci, vol. 3, no. 1, pp. 7–12, Mar. 2024.

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