|
|
|
| Research on Usefulness Recognition of Tourism Online Reviews Based on Multimodal Data Semantic Fusion |
| Ma Chao, Li Gang, Chen Sijing, Mao Jin, Zhang Ji |
| Center for Studies of Information Resources, Wuhan University, Wuhan 430072 |
|
|
|
|
Abstract In order to identify the influencing factors of multimodal data in online reviews of tourism products effectively, the online tourism product optimization method based on user-generated content is explored. From the perspective of data fusion analysis, we apply feature fusion methods to multimodal data in the online review of tourism products. Based on real online review data of tourism products, first, we performed a descriptive statistical analysis. Then, we utilized machine learning and deep learning methods, performed text vector embedding, image content recognition, and fusion of graphic feature vectors. Finally, we constructed multimodal online comment usefulness to classify models for model testing. The experimental results show that compared with single-modal comments containing text only or images only, the multimodal comments combining images and texts can predict the usefulness of online reviews better, and combine the incentive mechanism of comments better to improve the quality of online comments. In addition, the multimodal comments combining images and texts can be used to leverage the potential value of user-generated content, provide optimization ideas for product providers, and provide decision support for product consumers.
|
|
Received: 01 July 2019
|
|
|
|
1 张补宏, 周旋, 广新菊. 国内外旅游在线评论研究综述[J]. 地理与地理信息科学, 2017, 33(5): 119-126.
2 徐峰. 在线信息对不同城市类型酒店网上预订量的影响研究——以携程网数据为例[J]. 商业经济与管理, 2013(7): 73-80.
3 张思豆, 李君轶, 魏欢. 旅游电商平台在线评论的可信度研究——以“阿里旅行·西安东线一日游”旅游产品为例[J]. 旅游学刊, 2016, 31(11): 74-84.
4 MudambiS M, SchuffD. What makes a helpful review? A study of customer reviews on Amazon. com[J]. MIS Quarterly, 2010, 34(1): 185-200.
5 SussmanS W, SiegalW S. Informational influence in organizations: An integrated approach to knowledge adoption[J]. Information Systems Research, 2003, 14(1): 47-65.
6 卓四清, 冯永洲. 在线评论有用性影响因素实证研究——基于Tripadvisor.com酒店评论数据[J]. 现代情报, 2015, 35(4): 52-56, 74.
7 GhoseA, IpeirotisP G. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(10): 1498-1512.
8 殷国鹏. 消费者认为怎样的在线评论更有用?——社会性因素的影响效应[J]. 管理世界, 2012(12): 115-124.
9 KorfiatisN, García-BariocanalE, Sánchez-AlonsoS. Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content[J]. Electronic Commerce Research and Applications, 2012, 11(3): 205-217.
10 吴江, 刘弯弯. 什么样的评论更容易获得有用性投票——以亚马逊网站研究为例[J]. 数据分析与知识发现, 2017(9): 16-27.
11 黄麒, 张心雨, 刘嘉月. 基于在线购物数据评论的有用性影响因素研究[J]. 中国市场, 2017(1): 28-31.
12 闵庆飞, 覃亮, 张克亮. 影响在线评论有用性的因素研究[J]. 管理评论, 2017, 29(10): 95-107.
13 霍红, 张晨鑫. 在线评论质量丰富度对购买异质性产品的影响[J]. 企业经济, 2018, 37(6): 77-83.
14 汪旭晖, 陈鑫. 用户生成内容的图文匹配对消费者感知有用性的影响[J]. 管理科学, 2018, 31(1): 101-115.
15 MaY F, XiangZ, DuQ 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.
16 Baltru?aitisT, AhujaC, MorencyL P. Multimodal machine learning: A survey and taxonomy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(2): 423-443.
17 BernardiR, CakiciR, ElliottD, et al. Automatic description generation from images: A survey of models, datasets, and evaluation measures[J]. Journal of Artificial Intelligence Research, 2016, 55: 409-442.
18 MaoJ H, XuW, YangY, et al. Deep captioning with multimodal recurrent neural networks (m-RNN)[OL]. https://arxiv.org/abs/1412.6632.
19 Vinyals O, Toshev A, Bengio S,et al. Show and tell: A neural image caption generator[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2015: 3156-3164.
20 Song Y, Shi S M, Li J,et al. Directional skip-gram: Explicitly distinguishing left and right context for word embeddings[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2018, 2: 175-180.
21 Vaswani A, Shazeer N, Parmar N,et al. Attention is all you need[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems. 2017: 5998-6008.
22 DevlinJ, ChangM W, LeeK, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[OL]. https://arxiv.org/abs/1810.04805.
23 HubelD H, WieselT N. Receptive fields and functional architecture of monkey striate cortex[J]. The Journal of Physiology, 1968, 195(1): 215-243
24 He K M, Zhang X Y, Ren S Q,et al. Deep residual learning for image recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 770-778.
25 Huang G, Liu Z, van der Maaten L,et al. Densely connected convolutional networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2017: 3.
26 Szegedy C, Vanhoucke V, Ioffe S,et al. Rethinking the inception architecture for computer vision[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 2818-2826.
27 IandolaF N, HanS, MoskewiczM W, et al. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size[OL]. https://arxiv.org/abs/1602.07360.
28 刘宪立, 赵昆. 在线评论有用性关键影响因素识别研究[J]. 现代情报, 2017, 37(1): 94-99, 105.
29 张鑫, 朱振中. 在线评论有用性影响因素研究综述[J]. 商业经济研究, 2017(17): 82-84.
30 秦芬, 李扬. 用户生成内容激励机制研究综述及展望[J]. 外国经济与管理, 2018, 40(8): 141-152.
31 SunY C, DongX J, McIntyreS. Motivation of user-generated content: Social connectedness moderates the effects of monetary rewards[J]. Marketing Science, 2017, 36(3): 329-337.
32 ToubiaO, StephenA T. Intrinsic vs. image-related utility in social media: Why do people contribute content to twitter?[J]. Marketing Science, 2013, 32(3): 368-392. |
|
|
|
