Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (11): 19-27    DOI: 10.11925/infotech.2096-3467.2018.0835
Current Issue | Archive | Adv Search |
Examining Consumer Reviews of Overseas Shopping APP with Sentiment Analysis
Yang Zhao(),Qiqi Li,Yuhan Chen,Wenhang Cao
School of Information Management, Wuhan University, Wuhan 430072, China
Download: PDF(1546 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      

[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

Cite this article:

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

URL:     OR

[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.
[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.)
[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.
[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.)
[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.
[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.)
[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.
[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.
[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.)
[1] Zhongxi You,Weina Hua,Xuelian Pan. Matching Book Reviews and Essential Sentiment Lexicons with Chinese Word Segmenters[J]. 数据分析与知识发现, 2019, 3(7): 23-33.
[2] Cuiqing Jiang,Yibo Guo,Yao Liu. Constructing a Domain Sentiment Lexicon Based on Chinese Social Media Text[J]. 数据分析与知识发现, 2019, 3(2): 98-107.
[3] Bengong Yu,Peihang Zhang,Qingtang Xu. Selecting Products Based on F-BiGRU Sentiment Analysis[J]. 数据分析与知识发现, 2018, 2(9): 22-30.
[4] Ziming Zeng,Qianwen Yang. Sentiment Analysis for Micro-blogs with LDA and AdaBoost[J]. 数据分析与知识发现, 2018, 2(8): 51-59.
[5] Xiufang Wang,Shu Sheng,Yan Lu. Analyzing Public Opinion from Microblog with Topic Clustering and Sentiment Intensity[J]. 数据分析与知识发现, 2018, 2(6): 37-47.
[6] Sinan Yang,Jian Xu,Pingping Ye. Review of Online Sentiment Visualization Techniques[J]. 数据分析与知识发现, 2018, 2(5): 77-87.
[7] Tingting Wang,Kaiping Wang,Guijie Qi. Analyzing Implemented Ideas from Open Innovation Platform with Sentiment Analysis: Case Study of Salesforce[J]. 数据分析与知识发现, 2018, 2(4): 38-47.
[8] Yue He,Can Zhu. Sentiment Analysis of Weibo Opinion Leaders——Case Study of “Illegal Vaccine” Event[J]. 数据分析与知识发现, 2017, 1(9): 65-73.
[9] Hongli Zhang,Jiying Liu,Sinan Yang,Jian Xu. Predicting Online Users’ Ratings with Comments[J]. 数据分析与知识发现, 2017, 1(8): 48-58.
[10] Ge Gao,Junmei Luo,Yu Wang. Analyzing Textual Sentiment Based on HNC Theory[J]. 数据分析与知识发现, 2017, 1(8): 85-91.
[11] Huanrong Shou,Shuqing Deng,Jian Xu. Detecting Online Rumors with Sentiment Analysis[J]. 数据分析与知识发现, 2017, 1(7): 44-51.
[12] Chuanming Yu,Bolin Feng,Lu An. Sentiment Analysis in Cross-Domain Environment with Deep Representative Learning[J]. 数据分析与知识发现, 2017, 1(7): 73-81.
[13] Xinhui Dun,Yunqiu Zhang,Kaixi Yang. Fine-grained Sentiment Analysis Based on Weibo[J]. 数据分析与知识发现, 2017, 1(7): 61-72.
[14] Weifang Wu,Baojun Gao,Haixia Yang,Hanlin Sun. The Impacts of Reviews on Hotel Satisfaction: A Sentiment Analysis Method[J]. 数据分析与知识发现, 2017, 1(3): 62-71.
[15] Shuang Yang,Fen Chen. Analyzing Sentiments of Micro-blog Posts Based on Support Vector Machine[J]. 数据分析与知识发现, 2017, 1(2): 73-79.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938