Open Access

ARTICLE

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

Muhammad Zubair Asghar¹, Fazli Subhan², Muhammad Imran¹, Fazal Masud Kundi¹, Adil Khan³, Shahboddin Shamshirband^{4, 5, *}, Amir Mosavi^{6, 7, 8}, Peter Csiba⁸, Annamaria R. Varkonyi Koczy⁸

1 Institute of Computing and Information Technology, Gomal University, DIKhan, 29050, Pakistan.
2 National University of Modern Languages, Islamabad, Pakistan.
3 Higher Education Commission, Khyber Pakhtunkhwa, Pakistan.
4 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh, Vietnam.
5 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh, Vietnam.
6 Kando Kalman Faculty of Electrical Engineering, Obuda University, Budapest, Hungary.
7 Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, 99423, Germany.
8 Department of Mathematics and Informatics, J. Selye University, Komarno, 94501, Slovakia.

* Corresponding Author: Shahboddin Shamshirband. Email: .

Computers, Materials & Continua 2020, 63(3), 1093-1118. https://doi.org/10.32604/cmc.2020.07709

Received 20 June 2019; Accepted 29 July 2019; Issue published 30 April 2020

Abstract

Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

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Citations