Emotion Aware Machine Learning

Automated text analysis has evolved from basic sentiment analysis (positive, negative, neutral) to the more sophisticated field of Affective Computing. This paper reviews this evolution, focusing on the detection of complex emotions like sarcasm, nostalgia, boredom, and frustration, which are beyond the scope of simple polarity models. We trace the development from lexicon-based methods to modern deep learning architectures, particularly Transformer-based models. The review examines the psychological theories underpinning emotion classification, such as those by Ekman and Plutchik, and discusses the methodologies, datasets, and performance benchmarks for detecting complex emotions. We explore applications in human-computer interaction, mental health, and customer service, while also critically addressing the profound ethical challenges of algorithmic bias, privacy, and potential misuse. The paper concludes that while advanced models show great promise, significant challenges in data, context, and ethics must be addressed for responsible deployment.