Title: Content and opinion-enhanced neural model for opinion sentence classification of Chinese microblog comments

Authors: Yan Xiang; Junjun Guo; Yuxin Huang; Zhengtao Yu

Addresses: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China ' Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China ' Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China ' Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

Abstract: Opinion sentence classification of Chinese microblog comments aims to recognise those comments with opinions about the specific microblog content, which is the basis of internet public opinion analysis and opinion mining. However, existing opinion sentence classification methods do not consider whether the opinion sentences point at concerned objects or not. To address these issues, we propose a novel neural model, which combines the microblog content relevance-enhanced module and the opinion representation-enhanced module. In the first module, we propose a mutual attention operation that enables the model to extract better features representing microblog content. In the second module, we employ sentiment word embedding and self-attention operations to enhance the ability of the model to extract the opinion features. We evaluate our model using a Chinese microblog comment dataset. Experimental results show that the accuracy of the proposed model is 2%-5% higher than that of the baseline models, which shows that the proposed content relevance-enhancement and attention mechanism are beneficial to this task.

Keywords: opinion sentence; classification; microblog; neural model; attention operation.

DOI: 10.1504/IJICT.2023.134834

International Journal of Information and Communication Technology, 2023 Vol.23 No.4, pp.371 - 387

Received: 18 Aug 2020
Accepted: 10 Oct 2021
Published online: 14 Nov 2023 *

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