Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era

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Khin Sandar Kyaw
Praman Tepsongkroh
Chanwut Thongkamkaew
Farida Sasha

Abstract

Since trading has been transformed into online platforms, marketing strategies have adapted to digital systems in order to enhance the Customer Relationship Management (CRM) in the E-commerce era. E-commerce systems are the most widely used digital platforms where customer information including personal, and behavioral information, flows as a big data stream. Conducting business intelligent observation on digital big data assists to improve digital marketing policy through the customer intention prediction, decision-making to advertise based on the target group clustering, and customer assist recommendation. To discover the business intelligent, sentiment analysis technology can assist as a solution to understand the customer behavior through the opinion mining where the natural language processing, text analysis, computational linguistics, and biometrics are conducted to analysis the customer information and feedbacks, for smart digital marketing applications. This research observes the applications of sentiment analysis in E-commerce systems as a comprehensive study, and the critical role of discovering business intelligent for smart digital marketing in E-commerce platforms is pointed out according to the technical perspective. Furthermore, the concept of a business intelligent framework integrated with the modelling of decision-making, prediction, and recommendation systems using the contribution of hybrid feature selection which is based on rule-based and machine learning-based sentiment analysis, is proposed for the future innovative smart digital marketing trend.

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How to Cite
Khin Sandar Kyaw, Tepsongkroh, P. ., Thongkamkaew, C. ., & Sasha, F. . (2023). Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era. Asia Social Issues, 16(3), e252965. https://doi.org/10.48048/asi.2023.252965
Section
Research Article

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