A Review on Consumer Behavior Towards Online Shopping using Machine Learning


  • Waqas Bangyal Kohsar University Murree, Punjab, Pakistan
  • Adnan Ashraf School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China https://orcid.org/0000-0002-4544-6749
  • Rabia Shakir Department of Computer Science,Federal Urdu University of Science and Technology, Islamabad, Pakistan
  • Najeeb Ur Rehaman Department of Computer Science, University of Gujrat, Pakistan https://orcid.org/0000-0003-1662-2069




Sentiment Analysis, Machine Learning, Natural Language Processing, Data Visualization, Market Strategies


Study of consumer behavior in online shopping, as a rule, manages identification of consumers and their purchasing behavior. The purpose of such studies is to verify who purchases where, what, when, and how. The analysis of such consumer behavior is useful to get the buyer's prerequisites and requirements for their future aims towards the product. Through this review, E-commerce organizations can follow the utilization and sentiments appended to their items and adopt suitable promoting strategies to give a customized shopping experience to their buyers, consequently expanding their hierarchical benefit. This paper purpose to utilize information-driven promoting models, for example, information perception, natural language processing, and AI models that help in getting the demographics of an association. Additionally, make recommender frameworks through cooperative filtering, sentiment analysis, and neural networks.




How to Cite

Bangyal, W. ., Ashraf, A. ., Shakir, R. ., & Ur Rehaman, N. . (2022). A Review on Consumer Behavior Towards Online Shopping using Machine Learning. International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence, 1(1), 105–114. https://doi.org/10.54938/ijemdcsai.2022.01.1.84



Review Article