Data Mining for E-Commerce and Social Media: Insights and Future Research Directions

Year : 2025 | Volume : 12 | Issue : 01 | Page : 14 23
    By

    Uvaish Akhter,

  • Arunesh Pratap Sing,

  • Ajay Kumar Yadav,

  1. Assistant Professor, Department of Computer Science and Engineering, Vidhyapeeth Institute of Science and Technology, Bhopal, Madhya Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, Vidhyapeeth Institute of Science and Technology, Bhopal, Madhya Pradesh, India
  3. Assistant Professor, Department Department of Computer Science and Engineering, Vidhyapeeth Institute of Science and Technology, Bhopalof Computer Science and Engineering, Vidhyapeeth institute of science and technology Bhopal, Madhya Pradesh, India

Abstract

The fast expansion of e-commerce and social media has heralded a new era of data-rich settings, with enormous quantities of user interactions, preferences, and transactions generated on a daily basis. Data mining has developed as a critical strategy for leveraging big datasets, allowing businesses to gain concrete knowledge and drive decision-making. Data mining in e-commerce improves operational efficiency and user pleasure by allowing for personalized recommendations, consumer segmentation, fraud detection, and dynamic pricing. Similarly, in social media, it drives sentiment analysis, influencer authorization, trend prediction, and content suggestion, transforming marketing strategies and engagement optimization. However, data mining application is not without its obstacles. Data privacy, quality, scalability, and concerns about ethics necessitate a balanced strategy to maximize benefits while minimizing hazards. The study investigates data mining techniques, tools, and software used in e-commerce and social media, as well as future initiatives to solve existing limits, providing a comprehensive view of the technology’s transformational potential.

Keywords: Data mining, e-commerce, social media, machine learning, personalization, sentiment analysis

[This article belongs to E-Commerce for Future & Trends ]

How to cite this article:
Uvaish Akhter, Arunesh Pratap Sing, Ajay Kumar Yadav. Data Mining for E-Commerce and Social Media: Insights and Future Research Directions. E-Commerce for Future & Trends. 2025; 12(01):14-23.
How to cite this URL:
Uvaish Akhter, Arunesh Pratap Sing, Ajay Kumar Yadav. Data Mining for E-Commerce and Social Media: Insights and Future Research Directions. E-Commerce for Future & Trends. 2025; 12(01):14-23. Available from: https://journals.stmjournals.com/ecft/article=2025/view=203967


References

  1. Pan H, Zhou H. Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce. Electron Commer Res. 2020 Jun; 20(2): 297–320.
  2. Junaedi WR, Muhdar HM, Firmansyah A. Consumer loyalty of Indonesia e-commerce SMEs: the role of social media marketing and customer satisfaction. Int J Data Netw Sci. 2022 Mar 1; 6(2): 383–90.
  3. Jiang G, Liu F, Liu W, Liu S, Chen Y, Xu D. Effects of information quality on information adoption on social media review platforms: Moderating role of perceived risk. Data Sci Manag. 2021 Mar 1; 1(1): 13–22.
  4. Alghanam OA, Al-Khatib SN, Hiari MO. Data mining model for predicting customer purchase behavior in e-commerce context. Int J Adv Comput Sci Appl. 2022; 13(2): 421–428.
  5. Dogan A, Birant D. Machine learning and data mining in manufacturing. Expert Syst Appl. 2021 Mar 15; 166: 114060.
  6. Moshkov M, Zielosko B, Tetteh ET. Selected data mining tools for data analysis in distributed environment. Entropy. 2022 Oct 1; 24(10): 1401.
  7. Islam MF, Ferdousi R, Rahman S, Bushra HY. Likelihood prediction of diabetes at early stage using data mining techniques. In Computer Vision and Machine Intelligence in Medical Image Analysis: International Symposium, ISCMM 2019. Singapore: Springer; 2020; 113–125.
  8. Neha K, Reddy MY. A Study on Applications of Data Mining. Int J Sci Technol Res. 2020 Feb; 9(02): 3385–3388.
  9. Dol SM, Jawandhiya PM. Use of data mining tools in educational data mining. In 2022 IEEE 5th International Conference on Computational Intelligence and Communication Technologies (CCICT). 2022 Jul 8; 380–387.
  10. Aleem A, Gore MM. Educational data mining methods: A survey. In 2020 IEEE 9th international conference on communication systems and network technologies (CSNT). 2020 Apr 10; 182–188.
  11. Du Q, Li Y, Li Y, Zhou J, Cui X. Data mining of social media for urban resilience study: A case of rainstorm in Xi’an. Int J Disaster Risk Reduct. 2023 Sep 1; 95: 103836.
  12. Nanayakkara AC, Kumara BT, Rathnayaka RM. A survey of finding trends in data mining techniques for social media analysis. Sri Lanka Journal of Social Sciences and Humanities (SLJSSH). 2021 Aug 1; 1(2): 37–50.
  13. Zarrabeitia-Bilbao E, Jaca-Madariaga M, Rio-Belver RM, Álvarez-Meaza I. Nuclear energy: Twitter data mining for social listening analysis. Soc Netw Anal Min. 2023 Feb 6; 13(1): 29.
  14. Sirichanya C, Kraisak K. Semantic data mining in the information age: A systematic review. Int J Intell Syst. 2021 Aug; 36(8): 3880–916.
  15. Lahiani H, Frikha M. A systematic review of social media data mining on android. Procedia Comput Sci. 2023 Jan 1; 225: 2018–27.
  16. Nti IK, Quarcoo JA, Aning J, Fosu GK. A mini-review of machine learning in big data analytics: Applications, challenges, and prospects. Big Data Min Anal. 2022 Jan 25; 5(2): 81–97.
  17. Kumar D, Haque A, Mishra K, Islam F, Mishra BK, Ahmad S. Exploring the transformative role of artificial intelligence and metaverse in education: A comprehensive review. Metaverse Basic Appl Res. 2023; 2: 55.
  18. Kumar TS. Data mining based marketing decision support system using hybrid machine learning algorithm. J Artif Intell. 2020 Aug 28; 2(03): 185–93.
Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 14/02/2025
Accepted 17/02/2025
Published 19/03/2025
Publication Time 33 Days
185

Login


My IP

PlumX Metrics