@Article{cmc.2020.07709,
AUTHOR = {Muhammad Zubair Asghar, Fazli Subhan, Muhammad Imran, Fazal Masud Kundi, Adil Khan, Shahboddin Shamshirband, Amir Mosavi, Peter Csiba, Annamaria R. Varkonyi Koczy},
TITLE = {Performance Evaluation of Supervised Machine Learning  Techniques for Efficient Detection of Emotions from Online  Content},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1093--1118},
URL = {http://www.techscience.com/cmc/v63n3/38864},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.07709}
}