Vol.63, No.3, 2020, pp.1093-1118, doi:10.32604/cmc.2020.07709
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 Shamshirband4, 5, *, Amir Mosavi6, 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: .
Received 20 June 2019; Accepted 29 July 2019; Issue published 30 April 2020
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.
Emotion classification, machine learning classifiers, ISEAR dataset, performance evaluation.
Cite This Article
. , "Performance evaluation of supervised machine learning techniques for efficient detection of emotions from online content," Computers, Materials & Continua, vol. 63, no.3, pp. 1093–1118, 2020.
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