Enhancing Emotion Classification with a Hybrid BERT and CNN Architecture

In computational linguistics, effectively encoding and systematizing emotional expressions in language is a significant challenge. Existing machine learning (ML) models for text analysis often only recognize primary emotional states such as anger, happiness, and sadness, missing the more nuanced spectrum of human emotions. These models frequently overlook the complex semantic interplays and fail to capture the full diversity of emotional expressions, focusing instead on simplistic categorization. This paper proposes a robust ML framework that enhances emotion classification by discriminating among nuanced emotion categories—Anger, Joy, and Fear. Leveraging a sophisticated combination of Convolutional Neural Networks (CNNs) and the Bidirectional Encoder Representations from Transformers (BERT) architecture, our framework demonstrates exceptional accuracy in emotion detection. Evaluating a diverse dataset of text samples, each meticulously tagged with its expressed emotion, confirms the model’s superior performance in recognizing and classifying a broad range of emotional states.