Convolution SSM model for text emotion classification
Jiaxin Shi,
Mingyue Xiang
Abstract:In the pursuit of advanced human-machine interactions, the ability to detect emotions in textual data emerges as a crucial element for imbuing machines with empathetic communication capabilities. This paper proposes a theoretical framework for the Convolution Selective State Space Model (ConvSSM), a deep learning model designed to discern and classify the emotions conveyed through text. Unlike conventional analysis models, the ConvSSM is designed to accommodate a wide array of emotional expressions, thereby ca… Show more
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