Performance Evaluation Of Supervised Machine Learning Techniques For Efficient Detection Of Emotions From Online Content
Muhammad Zubair Asghar, Fazli Subhan, Muhammad Ali Imran, Fazal Masud Kundi, Adil Khan, Shahboddin Shamshirband, Amir Mosavi, Annamaria R. Varkonyi Koczy, Péter Csiba
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.