Emotion Detection in Reddit: Comparative Study of Machine Learning and Deep Learning Techniques

Emotion detection is pivotal in human communication, as it significantly influences behavior, relationships, and decision-making processes. This study concentrates on text-based emotion detection by leveraging the GoEmotions dataset, which annotates Reddit comments with 27 distinct emotions. These emotions are subsequently mapped to Ekman's 6 basic categories: joy, anger, fear, sadness, disgust, and surprise. We employed a range of models for this task, including 6 machine learning models, 3 ensemble models, and Long Short-Term Memory (LSTM) model to determine the optimal model for emotion detection. Results indicate that the Stacking classifier outperforms other models in accuracy and performance. Finally, the Stacking classifier is deployed via a Streamlit web application, underscoring its potential for real-world applications in text-based emotion analysis. Keywords: Text Based Emotion Detection, Machine Learning, Ensemble Learning, Deep Learning, GoEmotions, EmoBERTa, Streamlit Introduction Emotions are complex, subjective experiences, often linked to psychological states such as mood, temperament, and personality. These experiences influence human behavior, impacting decision-making, reactions to stimuli, and interpersonal interactions. In the contemporary world, where mental health disorders such as stress, anxiety, and depression are increasingly prevalent, understanding emotions is more important than ever (Maruf et al., 2024).