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Article type: Research Article
Authors: Zhou, Lixin | Zhou, Kexin | Liu, Chen; *
Affiliations: Business School, University of Shanghai for Science and Technology, Shanghai, China
Correspondence: [*] Corresponding author. Chen Liu, Business School, University of Shanghai for Science and Technology, Shanghai, 20093, China. E-mail: chenliu@usst.edu.cn.
Abstract: Stance detection is the task of classifying user reviews towards a given topic as either supporting, denying, querying, or commenting (SDQC). Most approaches for solving this problem use only the textual features, including the linguistic features and users’ vocabulary choice. A few approaches have shown that information from the network structure like graph model can add value, in addition to the textual features, by providing social connections and interactions that may be vital for the stance detection task. In this paper, we present a novel model that combines the text features with the network structure by (1) creating a graph-structure model based on conversational structure towards specific topics and (2) constructing a tree-gated neural network model (TreeGGNN) to capture structure information among reviews. We evaluate our model on four baseline models, which shows that the combination of text and network can achieve an improvement of 2–6% over the state-of-the-art baselines.
Keywords: Stance detection, gated graph neural network, deep learning, structure of conversation thread
DOI: 10.3233/JIFS-221953
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1703-1714, 2023
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