Convolution SSM model for text emotion classification
Jiaxin Shi, Mingyue Xiang · 2024
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 capturing the complexity inherent in textual emotional states. Experiments show that our model has better performance.