基于多维度图卷积网络的旅游评论有用性识别被引量：1

Usefulness Detection of Travel Reviews Based on Multi-dimensional Graph Convolutional Networks

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摘要【目的】利用深度学习模型识别旅游评论的有用性,给予消费者和酒店管理者商业决策参考。【方法】提出多维度图卷积网络和多模态融合的有用性识别模型,使用BERT和MAE模型分别对文本和图片进行预训练,利用多维度图卷积网络对多模态特征进行建模,再通过注意力机制捕捉多模态间的交互信息,最后融入文本特征进行评论有用性识别。【结果】在Yelp数据集上进行对比实验,结果表明所提模型识别准确率为73.21%,相较于传统单模态和现有多模态模型平均提升了10%。【局限】仅在Yelp数据集上尝试文本和图片两种模态,其他数据融合以及更多模态有待研究。【结论】所提模型将多维度的图卷积网络和多模态特征融入评论有用性识别中,可以有效提升识别的效果。 [Objective]This paper develops a new deep learning model to decide the usefulness of travel reviews,which provides valuable insights for consumers and hotel managers.[Methods]We proposed a usefulness identification model based on multi-dimensional graph convolutional networks and multi-modal fusion.Then,we used BERT and MAE models to pre-train texts and images,and adopted multi-view graph convolutional networks to model multi-modal features.Third,we captured the interactive information between different modalities with the attention mechanism.Finally,we integrated text features to identify valuable reviews.[Results]We conducted comparative experiments on the Yelp dataset.The accuracy of this method reached 73.21%,which was 10%higher than the traditional single-modal and existing multi-modal models.[Limitations]This paper only explores the text and image modalities on the Yelp dataset.More research is needed to investigate other data fusion and modalities.[Conclusions]The proposed model could effectively identify helpful online reviews with multi-dimensional graph convolutional networks and multi-modal features.

作者刘洋丁星辰马莉莉王淳洋朱立芳 Liu Yang;Ding Xingchen;Ma Lili;Wang Chunyang;Zhu Lifang(School of Information Management,Wuhan University,Wuhan 430072,China;Big Data Research Institute,Wuhan University,Wuhan 430072,China;School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China;Economics and Management School,Wuhan University,Wuhan 430072,China;School of Humanities and Communication,Guangdong University of Finance&Economics,Guangzhou 510320,China)

机构地区武汉大学信息管理学院武汉大学大数据研究院武汉大学国家网络安全学院武汉大学经济与管理学院广东财经大学人文与传播学院

出处《数据分析与知识发现》 CSSCI CSCD 北大核心 2023年第8期95-104,共10页 Data Analysis and Knowledge Discovery

基金国家自然科学基金青年项目(项目编号:72204190) 教育部人文社会科学研究青年基金项目(项目编号:22YJZH114)的研究成果之一。

关键词多模态特征多维度图卷积网络旅游评论有用性识别 Multi-modal Features Multi-dimensional Graph Convolutional Network Travel Reviews Usefulness Detection

分类号 TP391 [自动化与计算机技术—计算机应用技术] G350 [自动化与计算机技术—计算机科学与技术]