Usefulness Detection of Travel Reviews Based on Multi-dimensional Graph Convolutional Networks
Liu Yang1,2,Ding Xingchen3(),Ma Lili4,Wang Chunyang1,Zhu Lifang5
1School of Information Management, Wuhan University, Wuhan 430072, China 2Big Data Research Institute, Wuhan University, Wuhan 430072, China 3School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China 4Economics and Management School, Wuhan University, Wuhan 430072, China 5School of Humanities and Communication, Guangdong University of Finance & Economics, Guangzhou 510320, China
[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.
刘洋, 丁星辰, 马莉莉, 王淳洋, 朱立芳. 基于多维度图卷积网络的旅游评论有用性识别*[J]. 数据分析与知识发现, 2023, 7(8): 95-104.
Liu Yang, Ding Xingchen, Ma Lili, Wang Chunyang, Zhu Lifang. Usefulness Detection of Travel Reviews Based on Multi-dimensional Graph Convolutional Networks. Data Analysis and Knowledge Discovery, 2023, 7(8): 95-104.
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