Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content
This work provides a comparative analysis for emotion detection from online content, which is incremental as it focuses on improving existing methods rather than introducing new ones.
The paper evaluated multiple supervised machine learning classifiers on a benchmark emotion dataset to address performance degradation in emotion detection from text, finding that one classifier outperformed others in precision, recall, and f-measure.
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.