Title: Chinese sentiment analysis with multi-granularity vector representation and multi-channel network

Authors: Suoyu Zhang; Yong Wang; Xinyi Lyu

Addresses: School of Artificial Intelligence (Liangjiang), Chongqing University of Technology, Banan District, Chongqing 401320, China ' School of Artificial Intelligence (Liangjiang), Chongqing University of Technology, Banan District, Chongqing 401320, China ' School of Artificial Intelligence (Liangjiang), Chongqing University of Technology, Banan District, Chongqing 401320, China

Abstract: Aiming at the problem that normal vector representation method cannot fully represent the emotional semantic information contained in the text when dealing with Chinese text sentiment analysis task, a multi-granularity convolutional capsule neural network model is constructed. The input of sentiment analysis model is vector representation of the texts trained through the language model. As there exists the problem that a single language model is not enough to abstract text features, a multi-granularity text vector representation method based on BERT is proposed. The text vector representations with different granularities are input into the improved multi-channel convolutional capsule neural network. The capsule layer can associate the low-level and high-level text features, and it will extract information selectively through the dynamic routing algorithm to construct emotional and semantic features of the whole text. Multiple comparative experiments confirm that the method proposed in this paper will efficiently improve the accuracy of Chinese sentiment analysis.

Keywords: multi-granularity; vector representation; multi-channel; convolutional; capsule layer.

DOI: 10.1504/IJWMC.2022.124823

International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.3/4, pp.319 - 327

Received: 10 Oct 2021
Accepted: 29 Jan 2022
Published online: 09 Aug 2022 *

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