Title: Implicit emotional tendency recognition based on disconnected recurrent neural networks

Authors: Yiting Yan; Zhenghong Xiao; Zhenyu Xuan; Yangjia Ou

Addresses: College of Computer Science, Guangdong Polytechnic Normal University, 510665, China ' College of Computer Science, Guangdong University of Foreign Studies, 510006, China ' School of Information Science and Technology, Guangdong University of Foreign Studies, Guangdong, China ' College of Computer Science, Guangdong Polytechnic Normal University, 510665, China

Abstract: Implicit emotional tendency recognition is a challenging task, for the patterns of implicit emotional sentences have no obvious sentiment words or privative words. Currently, deep learning approaches have shown a huge potential for this problem. This paper proposes a hierarchical disconnected recurrent neural network to overcome the problem of the lack of emotional information in implicit sentiment sentence recognition. The proposed network first encodes both words and characters in implicit sentiment sentence by using the disconnected recurrent neural network and then fuses the context information of this sentence through the hierarchical structure. The capsule network is adopted to construct different fine-grained context information for extracting high-level feature information and provide additional semantic information for emotion recognition by using the context information. This approach improves the accuracy in implicit emotion recognition. Experimental results prove that the proposed model outperform various current mainstream models. The F₁ of ours reaches 81.5%, which is 2%-3% higher than those of the current mainstream models.

Keywords: disconnected recurrent neural network; implicit emotion; capsule network; sentiment orientation identification.

DOI: 10.1504/IJCSE.2021.113616

International Journal of Computational Science and Engineering, 2021 Vol.24 No.1, pp.1 - 8

Received: 05 Oct 2019
Accepted: 10 Jan 2020
Published online: 15 Mar 2021 *

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