Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 31 July 2019 / Approved: 2 August 2019 / Online: 2 August 2019 (08:49:27 CEST)

How to cite: Asghar, M.Z.; Subhan, F.; Imran, M.; Kundi, F.M.; Shamshirband, S.; Mosavi, A.; Csiba, P.; R. Várkonyi-Kóczy, A. Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content. Preprints 2019, 2019080019 (doi: 10.20944/preprints201908.0019.v1). Asghar, M.Z.; Subhan, F.; Imran, M.; Kundi, F.M.; Shamshirband, S.; Mosavi, A.; Csiba, P.; R. Várkonyi-Kóczy, A. Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content. Preprints 2019, 2019080019 (doi: 10.20944/preprints201908.0019.v1).

Abstract

Emotion detection from the text is an important and challenging problem in 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.

Subject Areas

emotion classification; machine learning classifiers; ISEAR dataset; data mining; performance evaluation; data science; opinion-mining

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