Performance Evaluation of Supervised Machine Learning Approaches for Emotion Detection in Social Media Text

Emotion detection on social media has become an essential task for understanding public opinion, user behavior, and emerging trends. Emotions are feelings that a human can explore via comments, likes, shares, and various social media interactions. This study presents a comparative analysis of different supervised machine learning methods for emotion classification in social media texts. It utilizes benchmark datasets from Twitter and Reddit to evaluate models such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Logistic Regression (LR). The study examines standard preprocessing techniques, including tokenization, stop-word removal, and TF-IDF vectorization. Results indicate that SVM outperforms other models in terms of F1-score and accuracy, especially in multi-class emotion detection. This research provides insights into the effectiveness of supervised learning techniques, detailed sentiment analysis of datasets, and offers recommendations for future work in emotion-aware applications.