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

EasyChair Preprint 2298

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 from the online community including users and business organizations 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.

Keyphrases: Data Science, ISEAR dataset, Machine Learning Classifiers, Opinion Mining, emotion classification, performance evaluation

@booklet{EasyChair:2298,
  author    = {Muhammad Zubair Asghar and Fazli Subhan and Muhammad Imran and Fazal Masud Kundi and Shahaboddin Shamshirband and Amir Mosavi and Peter Csiba and Annamária R. Várkonyi-Kóczy},
  title     = {Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content},
  howpublished = {EasyChair Preprint 2298},
  year      = {EasyChair, 2020}}