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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (11): 19-27    DOI: 10.11925/infotech.2096-3467.2018.0835
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Examining Consumer Reviews of Overseas Shopping APP with Sentiment Analysis
Zhao Yang(), Li Qiqi, Chen Yuhan, Cao Wenhang
School of Information Management, Wuhan University, Wuhan 430072, China
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[Objective] This paper analyzes the sentiment of online reviews, and then evaluates the consumer’s satisfaction with overseas shopping APP, aiming to improve its performance. [Methods] First, we collected reviews of these APPs from the APP Store. Then, we clustered the APPs’ attributes with Canopy and K-means algorithms, which defines the evaluation dimensions of consumer’s satisfaction. Finally, we computed scores of the consumer’s satisfaction with the CNN-SVM sentiment analysis model. [Results] The most important factor affecting the consumer’s satisfaction with overseas shopping APP was commodities, followed by price, interaction, service, and logistics. The consumer’s satisfaction level with the vertical overseas shopping APP was higher than that of the overseas buyer APP and the comprehensive overseas shopping APP. The consumer’s satisfaction level is relatively low with logistics and services. [Limitations] More overseas shopping APP should be included in future research. [Conclusions] The sentiment analysis method is an effective way to analyze consumer’s satisfaction with online reviews of overseas shopping APP.

Key wordsSentiment Analysis      Overseas Shopping APP      Consumer Satisfaction      CNN-SVM     
Received: 26 July 2018      Published: 11 December 2018
ZTFLH:  TP391 G35  

Cite this article:

Zhao Yang,Li Qiqi,Chen Yuhan,Cao Wenhang. Examining Consumer Reviews of Overseas Shopping APP with Sentiment Analysis. Data Analysis and Knowledge Discovery, 2018, 2(11): 19-27.

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发表时间 用户ID 星级评分 评论标题 评论内容
2017-11-04 14:55:42 豐收F1 5 值得信赖 在几个知名网站都经常购买各种商品, 第一在网易考拉购买, 物流给力, 价格实惠。
2017-11-04 14:50:58 没有草莓牛奶就不开心的人 1 讨厌强制更新 就这样, 商品也不齐全。
2017-05-12 16:59:56 crjjzz 4 不错的网购APP 目前在这网购APP买的东西都蛮好用的, 都验过正品, 价格实惠在那里。
2017-10-25 14:41:15 无尽土豆泥 2 我选择淘宝 ……不仅没便宜, 甚至还贵了很多, 拿ns举例, 美亚和日亚便宜的才卖2000, 淘宝2600, 这上面居然3300……骗谁啊!
算法 准确率(Precision) 召回率
(F1 Score)
Decision Tree 79.18 79.91 79.85
SVM 91.32 87.31 89.22
CNN 94.28 91.59 92.87
CNN-SVM 97.79 96.21 96.97
APP类别 名称 商品 价格 物流 交互 服务
HIGO 74.52 86.25 78.35 57.89 61.67
洋码头 63.06 82.26 45.71 87.74 33.96
综合类 网易考拉 83.56 84.91 77.78 74.19 51.43
小红书 70.83 75.00 30.77 64.86 37.04
亚马逊 87.21 82.11 60.64 54.39 35.29
波罗蜜 86.96 84.34 49.33 66.20 30.95
折疯了 86.13 86.63 85.00 85.85 54.55
垂直类 宝贝格子 83.72 96.55 93.94 79.17 90.91
蜜芽 77.88 83.33 42.86 46.30 39.66
环球捕手 69.84 85.50 40.91 73.68 34.21
[1] 周涛, 方文侃. 移动社交APP位置分享服务持续使用研究[J]. 杭州电子科技大学学报: 社会科学版, 2016, 12(3): 1-7.
[1] (Zhou Tao, Fang Wenkan.On Sustained Usage of Mobile Social APP Location Sharing Service[J]. Journal of Hangzhou Dianzi University: Social Sciences, 2016, 12(3): 1-7.)
[2] 张一帆. 我国旅游APP用户满意度的实证研究[D]. 海口: 海南大学, 2015.
[2] (Zhang Yifan.Empirical Study of Customer Satisfaction About Tourism APP in China[D]. Haikou: Hainan University, 2015.)
[3] Dwikesumasari P R, Ervianty R M.Customer Loyalty Analysis of Online Travel Agency APP with Customer Satisfaction as a Mediation Variable[C]//Proceedings of the 2017 International Conference on Organizational Innovation. 2017.
[4] 明均仁, 张俊. 高校移动图书馆APP用户满意度影响因素[J]. 图书馆论坛, 2018, 38(4): 84-94.
[4] (Ming Junren, Zhang Jun.Research on the Factors Influencing User Satisfaction of University Mobile Library APP[J]. Library Tribune, 2018, 38(4): 84-94.)
[5] 李武, 赵星. 大学生社会化阅读APP持续使用意愿及发生机理研究[J]. 中国图书馆学报, 2016, 42(1): 52-65.
doi: 10.13530/j.cnki.jlis.160005
[5] (Li Wu, Zhao Xing.Understanding the Continuance Intention of Social Reading Apps by College Students[J]. Journal of Library Science in China, 2016, 42(1): 52-65.)
doi: 10.13530/j.cnki.jlis.160005
[6] Song B, Lee C, Park Y.Identifying Critical Factors for Customer Satisfaction in Mobile Application Service: A Semantic Text Mining and Bayesian Network Approach[J]. International Proceedings of Economics Development & Research, 2011, 8: 33-37.
[7] Pang B, Lee L, Vaithyanathan S.Thumbs Up? Sentiment Classification Using Machine Learning Techniques[C]// Proceedings of the 2002 ACL Conference on Empirical Methods in Natural Language Processing. 2002: 79-86.
[8] 史伟, 王洪伟, 何绍义. 基于语义的中文在线评论情感分析[J]. 情报学报, 2013, 32(8): 860-867.
doi: 10.3772/j.issn.1000-0135.2013.08.009
[8] (Shi Wei, Wang Hongwei, He Shaoyi.Sentiment Analysis of Chinese Online Reviews Based on Semantics[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(8): 860-867.)
doi: 10.3772/j.issn.1000-0135.2013.08.009
[9] 王洪伟, 宋媛, 杜战其, 等. 基于在线评论情感分析的快递服务质量评价[J]. 北京工业大学学报, 2017, 43(3): 402-412.
doi: 10.11936/bjutxb2016010063
[9] (Wang Hongwei, Song Yuan, Du Zhanqi, et al.Evaluation of Service Quality for Express Industry Through Sentiment Analysis of Online Reviews[J]. Journal of Beijing University of Technology, 2017, 43(3): 402-412.)
doi: 10.11936/bjutxb2016010063
[10] Yih W, He X, Meek X.Semantic Parsing for Single-Relation Question Answering[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 2014, 2: 643-648.
[11] Collobert R, Weston J, Bottou L, et al.Natural Language Processing (Almost) from Scratch[J]. Journal of Machine Learning Research, 2011, 12: 2493-2537.
[12] Kim Y.Convolutional Neural Networks for Sentence Classification[OL]. arXiv Preprint. arXiv: 1408.5882.
doi: 10.1109/CLEI.2017.8226381
[13] Mohamed A, Dahl G, Hinton G.Deep Belief Networks for Phone Recognition[C]// Proceedings of the 2009 NIPS Workshop on Deep Learning for Speech Recognition and Related Applications. 2009.
[14] Huang F J, Lecun Y.Large-scale Learning with SVM and Convolutional for Generic Object Categorization[C]// Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006: 284-291.
[15] Le Q V, Ngiam J, Chen Z, et al.Tiled Convolutional Neural Networks[C]// Proceedings of the 24th Annual Conference on Neural Information Processing Systems. 2010: 1279-1287.
[16] Coates A, Ng A Y, Lee H.An Analysis of Single-Layer Networks in Unsupervised Feature Learning[C]// Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. 2011.
[17] Rozi M F, Mukhlash I, Soetrisno, et al. Opinion Mining on Book Review Using CNN-L2-SVM Algorithm[C]// Proceedings of the 2018 International Conference on Mathematics: Pure, Applied and Computation. 2018. DOI :10.1088/1742-6596/974/1/012004.
[18] Socher R, Pennington J, Huang E H.Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions[C]// Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 2011: 151-161.
[19] Ren J, Yeoh W, Ee M S, et al.Online Consumer Reviews and Sales: Examining the Chicken-Egg Relationships[J]. Journal of the Association for Information Science & Technology, 2017, 69(3): 449-460.
doi: 10.1002/asi.23967
[20] Che W X, Li Z H, Liu T.LTP: A Chinese Language Technology Platform[C]// Proceedings of the 23rd International Conference on Computational Linguistics. 2010: 13-16.
[21] 赵志滨, 刘欢, 姚兰, 等. 中文产品评论的维度挖掘及情感分析技术研究[J]. 计算机科学与探索, 2018, 12(3): 341-349.
[21] (Zhao Zhibin, Liu Huan, Yao Lan, et al.Research on Dimensional Mining and Sentiment Analysis for Chinese Product Comments[J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(3): 341-349.)
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