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.
Ye Q, Law R, Gu B, et al. The Influence of User-Generated Content on Traveler Behavior: An Empirical Investigation on the Effects of E-Word-of-Mouth to Hotel Online Bookings[J]. Computers in Human Behavior, 2011, 27(2): 634-639.
doi: 10.1016/j.chb.2010.04.014
[2]
Chen C C, Tseng Y D. Quality Evaluation of Product Reviews Using an Information Quality Framework[J]. Decision Support Systems, 2011, 50(4): 755-768.
doi: 10.1016/j.dss.2010.08.023
[3]
Zhu L, Yin G P, He W. Is This Opinion Leader’s Review Useful? Peripheral Cues for Online Review Helpfulness[J]. Journal of Electronic Commerce Research, 2014, 15(4): 267-280.
[4]
Zhu W W, Cui P, Wang Z, et al. Multimedia Big Data Computing[J]. IEEE MultiMedia, 2015, 22(3). DOI: 10.1109/MMUL.2015.66.
[5]
Jain P K, Pamula R, Srivastava G. A Systematic Literature Review on Machine Learning Applications for Consumer Sentiment Analysis Using Online Reviews[J]. Computer Science Review, 2021, 41: 100413.
doi: 10.1016/j.cosrev.2021.100413
[6]
Tsai C F, Chen K C, Hu Y H, et al. Improving Text Summarization of Online Hotel Reviews with Review Helpfulness and Sentiment[J]. Tourism Management, 2020, 80: 104122.
doi: 10.1016/j.tourman.2020.104122
[7]
Diaz G O, Ng V. Modeling and Prediction of Online Product Review Helpfulness: A Survey[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers). 2018: 698-708.
[8]
Almagrabi H, Malibari A, McNaught J. A Survey of Quality Prediction of Product Reviews[J]. International Journal of Advanced Computer Science and Applications, 2015, 6(11): 49-58.
[9]
Weathers D, Swain S D, Grover V. Can Online Product Reviews be More Helpful? Examining Characteristics of Information Content by Product Type[J]. Decision Support Systems, 2015, 79: 12-23.
doi: 10.1016/j.dss.2015.07.009
[10]
Chua A Y K, Banerjee S. Helpfulness of User-Generated Reviews as a Function of Review Sentiment, Product Type and Information Quality[J]. Computers in Human Behavior, 2016, 54: 547-554.
doi: 10.1016/j.chb.2015.08.057
[11]
Zhang Y, Lin Z J. Predicting the Helpfulness of Online Product Reviews: A Multilingual Approach[J]. Electronic Commerce Research and Applications, 2018, 27: 1-10.
doi: 10.1016/j.elerap.2017.10.008
[12]
Siering M, Muntermann J, Rajagopalan B. Explaining and Predicting Online Review Helpfulness: The Role of Content and Reviewer-Related Signals[J]. Decision Support Systems, 2018, 108: 1-12.
doi: 10.1016/j.dss.2018.01.004
[13]
Gao B J, Hu N, Bose I. Follow the Herd or be Myself? An Analysis of Consistency in Behavior of Reviewers and Helpfulness of Their Reviews[J]. Decision Support Systems, 2017, 95: 1-11.
doi: 10.1016/j.dss.2016.11.005
[14]
Zhou S S, Guo B. The Order Effect on Online Review Helpfulness: A Social Influence Perspective[J]. Decision Support Systems, 2017, 93: 77-87.
doi: 10.1016/j.dss.2016.09.016
(Ma Chao, Li Gang, Chen Sijing, et al. Research on Usefulness Recognition of Tourism Online Reviews Based on Multimodal Data Semantic Fusion[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(2): 199-207.)
[16]
Cao Q, Duan W J, Gan Q W. Exploring Determinants of Voting for the “Helpfulness” of Online User Reviews: A Text Mining Approach[J]. Decision Support Systems, 2011, 50(2): 511-521.
doi: 10.1016/j.dss.2010.11.009
[17]
Salehan M, Kim D J. Predicting the Performance of Online Consumer Reviews: A Sentiment Mining Approach to Big Data Analytics[J]. Decision Support Systems, 2016, 81: 30-40.
doi: 10.1016/j.dss.2015.10.006
[18]
Kaushik K, Mishra R, Rana N P, et al. Exploring Reviews and Review Sequences on E-Commerce Platform: A Study of Helpful Reviews on Amazon.in[J]. Journal of Retailing and Consumer Services, 2018, 45: 21-32.
doi: 10.1016/j.jretconser.2018.08.002
[19]
Chen M J, Farn C K. Examining the Influence of Emotional Expressions in Online Consumer Reviews on Perceived Helpfulness[J]. Information Processing & Management, 2020, 57(6): 102266.
doi: 10.1016/j.ipm.2020.102266
[20]
Zhou Y S, Yang S Q, Li Y X, et al. Does the Review Deserve More Helpfulness When Its Title Resembles the Content? Locating Helpful Reviews by Text Mining[J]. Information Processing & Management, 2020, 57(2): 102179.
doi: 10.1016/j.ipm.2019.102179
[21]
Luo Y, Xu X. Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp[J]. Sustainability, 2019, 11(19): 5254.
doi: 10.3390/su11195254
[22]
Li J Q, Liu X K, Yin W P, et al. Empirical Evaluation of Multi-Task Learning in Deep Neural Networks for Natural Language Processing[J]. Neural Computing and Applications, 2021, 33(9): 4417-4428.
doi: 10.1007/s00521-020-05268-w
[23]
Du J H, Rong J, Wang H, et al. Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction[C]// Proceedings of International Conference on Web Information Systems Engineering. 2019: 795-809.
[24]
Zhang Y, Wallace B. A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification[OL]. arXiv Preprint, arXiv:1510.03820.
[25]
Chen C, Qiu M H, Yang Y F, et al. Multi-Domain Gated CNN for Review Helpfulness Prediction[C]// Proceedings of the World Wide Web Conference. 2019: 2630-2636.
[26]
Saumya S, Singh J P, Dwivedi Y K. Predicting the Helpfulness Score of Online Reviews Using Convolutional Neural Network[J]. Soft Computing, 2020, 24(15): 10989-11005.
doi: 10.1007/s00500-019-03851-5
[27]
Fan M, Feng C, Guo L, et al. Product-Aware Helpfulness Prediction of Online Reviews[C]// Proceedings of the World Wide Web Conference. 2019: 2715-2721.
[28]
Ma Y F, Xiang Z, Du Q Z, et al. Effects of User-Provided Photos on Hotel Review Helpfulness: An Analytical Approach with Deep Leaning[J]. International Journal of Hospitality Management, 2018, 71: 120-131.
doi: 10.1016/j.ijhm.2017.12.008
[29]
Kipf T N, Welling M. Semi-supervised Classification with Graph Convolutional Networks[OL]. arXiv Preprint, arXiv:1609.02907.
[30]
Soni U, Bhambhani M, Khapra M M. Network Embedding Using Hierarchical Feature Aggregation[C]// Proceedings of the 6th International Conference on Learning Representations. 2018.
[31]
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Volume 1 (Long and Short Papers). 2019: 4171-4186.
[32]
Shaw P, Uszkoreit J, Vaswani A. Self-attention with Relative Position Representations[OL]. arXiv Preprint, arXiv:1803.02155.
[33]
He K M, Chen X L, Xie S N, et al. Masked Autoencoders are Scalable Vision Learners[OL]. arXiv Preprint, arXiv:2111.06377.
[34]
Defferrard M, Bresson X, Vandergheynst P. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016: 3844-3852.
[35]
Cao S S, Lu W, Xu Q K. GraRep: Learning Graph Representations with Global Structural Information[C]// Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015: 891-900.
[36]
Luan Y D, Lin S F. Research on Text Classification Based on CNN and LSTM[C]// Proceedings of 2019 IEEE International Conference on Artificial Intelligence and Computer Applications. 2019: 352-355.
[37]
Huang Z H, Xu W, Yu K. Bidirectional LSTM-CRF Models for Sequence Tagging[OL]. arXiv Preprint, arXiv:1508.01991.
[38]
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[OL]. arXiv Preprint, arXiv:1409.1556.
[39]
Antol S, Agrawal A, Lu J S, et al. VQA: Visual Question Answering[C]// Proceedings of 2015 IEEE International Conference on Computer Vision. 2015: 2425-2433.
[40]
Jin Z W, Cao J, Guo H, et al. Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs[C]// Proceedings of the 25th ACM International Conference on Multimedia. 2017: 795-816.
[41]
Khattar D, Goud J S, Gupta M, et al. MVAE: Multimodal Variational Autoencoder for Fake News Detection[C]// Proceedings of ‘the World Wide Web Conference. 2019: 2915-2921.