Integrating commonsense knowledge with GPT embeddings for emotion classification

Recognizing emotions in text is still hard, especially in real life where little differences and hidden indications are widespread. Because they don't rely enough on either contextual understanding or external knowledge, traditional models typically miss the deeper layers of human emotion. This research suggests a new paradigm based on fusion that combines contextual semantics with common sense knowledge to improve emotion classification. We use GPT-based embeddings and external knowledge graphs like ConceptNet and COMET to help us grasp emotions in text both via experience and through meaning. Our technique makes important contributions by:

• Includes both contextual meaning and common sense reasoning to find emotions more accurately.

• It connects surface-level language clues with hidden emotional states.

• It makes it easier to sort emotions into seven main groups: Joy, Sadness, Anger, Fear, Surprise, Disgust, and Neutral.

A thorough examination of the GoEmotions dataset shows that our model does far better than current baselines at classifying multiple emotions. Combining commonsense and contextual aspects makes things easier to understand and more reliable, especially when dealing with indirect or unclear emotional expressions. Our results show how important it is to combine semantic knowledge with human-like experiential reasoning to make affective computing more accurate and useful in more situations.