Convolution SSM model for text emotion classification

Jiaxin Shi; Mingyue Xiang

doi:10.1117/12.3034918

11 July 2024 Convolution SSM model for text emotion classification

Jiaxin Shi, Mingyue Xiang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321021 (2024) https://doi.org/10.1117/12.3034918
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

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 capturing the complexity inherent in textual emotional states. Experiments show that our model has better performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiaxin Shi and Mingyue Xiang "Convolution SSM model for text emotion classification", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321021 (11 July 2024); https://doi.org/10.1117/12.3034918

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