Efficient Turkish Text Classification Approach for Crisis Management Systems

Authors : Saed ALQARALEH

Pages : 718-731

Doi:10.35378/gujs.715296

View : 10 | Download : 7

Publication Date : 2021-09-01

Article Type : Research Paper

Abstract :In this paper, an effective tweet classification system that fully supports the Turkish language has been developed. The proposed system can be used for mining insert ignore into journalissuearticles values(classifying); the recently published and publicly available tweets to find the crisis’s most related and useful tweets to gain situational awareness, which can help in taking the correct responses in order to prevent or at least decrease the effect of such situations. A deep study was carried out to improve and optimize the proposed system. In more detail, some intensive experiments were performed to investigate the performance of some well-known machine learning algorithms, i.e., K-Nearest Neighbor insert ignore into journalissuearticles values(KNN);, Support Vector Machine insert ignore into journalissuearticles values(SVM);, and Naive Bayes insert ignore into journalissuearticles values(NB); when used for text insert ignore into journalissuearticles values(tweets); classification. Then, the performances of the ensemble systems of the studied algorithms and the Random Forest insert ignore into journalissuearticles values(RF);, AdaBoost Classifier insert ignore into journalissuearticles values(AdaBoost);, GradientBoosting Classifier insert ignore into journalissuearticles values(GBC); ensemble systems have also been observed. As shown in the experimental evaluation and analysis, the proposed approach has stability, robustness, and can achieve quite good performance when processing the Turkish language. The performance of the proposed classifier was also compared with two state-of-the-art text classification approaches, i.e., `Empirical` and “Turkish Deep `.
Keywords : Crises management, Systems, Ensemble learning, Text classification