Türkçe Metinlerde Duygu Analizi için Farklı Makine Öğrenmesi Yöntemlerinin Karşılaştırılması

Abstract

In this research, the classification results of different Machine Learning Algorithms were compared on the validated TREMO data set used in the field of emotion extraction from Turkish texts. Emotion analysis was considered as text classification problem and four different machine algorithms, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) have been investigated. The categories provided by the data set, which are happiness, fear, anger, sadness, disgust and surprise, were used as emotion categories. In the preprocessing phase, stemming process was performed using the truncate at five (F5) method. After stemming process, the data set was modeled using the Vector Space Model. After that, the first 500 words for each emotion in the data set were identified by the Mutual Information (MI) formula. The comparison of classification results was based on accuracy metric. According to experimental study results, the ANN classifier was performed best, and SVM, RF and KNN performed, in descending order.

A study of Turkish emotion classification with pretrained language models

“…The Turkish emotion data set (TREMO), which is based on Ekman’s model and consists of 27,350 samples, was created as a result of a questionnaire conducted on 4709 people in the study [30, 31]. In the experiments performed on TREMO, the highest performance was achieved with the SVM with an 86% general accuracy.…”