Cross-Target Stance Detection with Sentiments-Aware Hierarchical Attention Network

作     者：Kelan Ren Facheng Yan Honghua Chen Wen Jiang Bin Wei Mingshu Zhang 

作者机构：College of Cryptographic EngineeringEngineering University of PAPXi’an710086China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第81卷第10期

页      面：789-807页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Social Science Fund of China(20BXW101) 

主　　题：Cross-target stance detection sentiment analysis commentary-level texts hierarchical attention network 

摘      要：The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language *** stance detection methods often struggle with understanding limited context and have insufficient generalization across diverse sentiments and semantic *** paper focuses on effectively mining and utilizing sentimentsemantics knowledge for stance knowledge transfer and proposes a sentiment-aware hierarchical attention network(SentiHAN)for cross-target stance *** introduces an improved hierarchical attention network designed to maximize the use of high-level representations of targets and texts at various fine-grain *** model integrates phrase-level combinatorial sentiment knowledge to effectively bridge the knowledge gap between known and unknown *** doing so,it enables a comprehensive understanding of stance representations for unknown targets across different sentiments and semantic *** model’s ability to leverage sentimentsemantics knowledge enhances its performance in detecting stances that may not be directly observable from the immediate *** experimental results indicate that SentiHAN significantly outperforms existing benchmark methods in terms of both accuracy and ***,the paper employs ablation studies and visualization techniques to explore the intricate relationship between sentiment and *** analyses further confirm the effectiveness of sentence-level combinatorial sentiment knowledge in improving stance detection capabilities.