Emotion recognition from textual data has become a crucial task in understanding user sentiment, behavioral in-sights, and social media interactions. This study presents a comprehensive comparison of Natural Language Processing (NLP) techniques encompassing rule-based systems, lexi-con-driven models, and advanced ma-chine learning algorithms such as Sup-port Vector Machines (SVM) and Recur-rent Neural Networks (RNNs) for extracting emotions from diverse text sources. Utilizing a multi-domain la-beled dataset, we evaluate the performance of each technique based on con-tent type (e.g., news vs. social media) and the complexity of emotion granularity (e.g., basic vs. subtle emotions). The study underscores the importance of feature engineering, data balancing, and hyperparameter tuning in achieving optimal performance. Error analysis further reveals context-dependent challenges and limitations across models. Our findings advocate for hybrid and context-aware approaches to emotion detection, highlighting that no single method universally outperforms others. This work lays a foundation for future explorations into deep learning integration and ensemble modeling to capture the full spectrum of emotional expressions in textual content.
Emotion Detection, Natural Language Processing, Lexicon-Based Methods, Machine Learning, Sentiment Analysis, Text Mining, Deep Learning, Feature Engineering, SVM, RNN, Emotion Classi-fication, Context-Aware NLP.