Techniques of Ensemble Learning for Human Emotional Classification and Detection

The identification of emotions from textual data has attracted attention due to developments in natural language processing (NLP), which provides insightful information about human behaviour in a variety of contexts. This study evaluates the performance of several Using an emotion dataset that is available on Hugging Face Hub, machine learning methods, such as Support Vector Machine (SVM), Logistic Regression, Random Forest, and Multinomial Naive Bayes, are used to classify emotions. The models we tested had different accuracy percentages: multinomial naive Bayes produced 65.90%, logistic regression performed well at 82.40%, random forest produced 85.12%, and support vector machine (SVM) produced 81.56%. Moreover, utilizing ensemble learning methodologies to capitalize on the advantages of many models has improved overall efficiency. Our ensemble learning approach demonstrates the effectiveness of mixing multiple models for improved emotion recognition, achieving an amazing 98% accuracy. Ensemble learning is a promising approach for emotion identification in textual data because it leverages the combined knowledge of separate models, reducing flaws and increasing robustness. Keywords--- Machine learning, Support Vector Machine, Random Forest, logarithmic regression, Multinomial Naive Bayes, Ensemble Voting Classifier, and Emotion Identification.