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

TL;DR

This paper evaluates various supervised machine learning classifiers on a benchmark dataset to identify the most effective method for emotion detection from online text, addressing limitations of previous approaches.

Contribution

It provides a comprehensive performance comparison of multiple classifiers on a standard emotion dataset, highlighting the most effective model for emotion detection.

Findings

Identified the classifier with the highest accuracy and F-measure.

Compared classifiers based on precision, recall, and F-measure.

Recommended the best-performing classifier for emotion detection tasks.

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.