期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2019
卷号：67
期号：9
页码：11-20
DOI：10.14445/22312803/IJCTT-V67I9P103
出版社：Seventh Sense Research Group
摘要：Recently, Retail 4.0 is higher demand for accurate prediction of consumer’s purchase intention. In this regard, an attribute level decision support prediction model has been created for providing an influential online shopping platform to the customers. In order to build the prediction model, brand’s social reviews’ polarity are calculated from social network mining and sentiment analysis, respectively. Afterward, an appropriate regression analysis and required instances have been found for each attribute to predict the appropriate product stats. One of the key findings, the camera attributes sensor, display, and image stabilization make the customer attention at the end of the search. The outcomes of this analysis can be profitable to online retailers and prepare an efficient platform for the customers to obtain the desired goods. Finally, the sensitivity analysis has also been done to test the robustness of the applied model.
关键词：attribute level decision support prediction model; regression analysis; social network mining; sentiment analysis; e-commerce retailers
