CFP last date
20 October 2025
Call for Paper
November Edition
IJCA solicits high quality original research papers for the upcoming November edition of the journal. The last date of research paper submission is 20 October 2025

Submit your paper
Know more
The week's pick
Responsive image
Zero Trust Architecture Implementation in Enterprise Networks: Evaluating Effectiveness Against Cyber Threats

Stephen Kofi Dotse Samuel Yao Sebuabe Augustus Obeng Silas Asani Abudu Edna Awisie Pappoe
Random Articles
Reseach Article

Igniting the Power of Social Media Analytics for Business Development and Growth

by S. Lakshmi, Kiruthiga R., Srividhya Murali
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 45
Year of Publication: 2025
Authors: S. Lakshmi, Kiruthiga R., Srividhya Murali
10.5120/ijca2025925681

S. Lakshmi, Kiruthiga R., Srividhya Murali . Igniting the Power of Social Media Analytics for Business Development and Growth. International Journal of Computer Applications. 187, 45 ( Sep 2025), 53-59. DOI=10.5120/ijca2025925681

@article{ 10.5120/ijca2025925681,
author = { S. Lakshmi, Kiruthiga R., Srividhya Murali },
title = { Igniting the Power of Social Media Analytics for Business Development and Growth },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2025 },
volume = { 187 },
number = { 45 },
month = { Sep },
year = { 2025 },
issn = { 0975-8887 },
pages = { 53-59 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number45/igniting-the-power-of-social-media-analytics-for-business-development-and-growth/ },
doi = { 10.5120/ijca2025925681 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-09-30T15:40:33.552679+05:30
%A S. Lakshmi
%A Kiruthiga R.
%A Srividhya Murali
%T Igniting the Power of Social Media Analytics for Business Development and Growth
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 45
%P 53-59
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the technological era, the social media platform has enormous power for providing huge amount of data to enhance the growth of business, optimize the marketing strategies and to prepare effective strategic plans to meet the real-world challenges. The social media how effectively utilized to understand the audience, identify and segment the influencers and customers based on the needs. The future trends and the performance of the customers by implementing the techniques such as artificial intelligence, machine learning, data visualization and predictive mechanism to improve the current techniques and strategic plans as well as to predict the future trends of the customer behaviour. The proactive responds can be taken by using the Key Performance Indicators such as sentiments, trends for fine-tuning their social media campaigns. Hence, social media analytics is considered as a powerful tool for business development and growth. With the right tools, strategies and techniques companies can unlock the potentials of social media to achieve long term sustainable growth and success. This chapter aims to start with the fundamental concepts of social media analytics and the applications of the social media data to influence the sustainability goals, business growth, performance and outcome.

References
  1. Ahuja, R., & Yadav, R. (2023). Machine learning techniques for social media data classification: A review. Journal of Intelligent Information Systems.
  2. Agarwal, P., Gupta, K., & Sen, R. (2023). Measuring influencer impact in social networks using SNA. Journal of Digital Marketing and Social Media.
  3. Bhuvanya, R., Mehta, N., & Singh, R. (2024). Predictive analytics in marketing: AI-based decision support using SMA. International Journal of Marketing Science.
  4. Bhuyan, M. A., Talukdar, P., & Rahman, T. (2024). Ethical frameworks in social media analytics using AI and machine learning. Journal of Business Ethics and Technology.
  5. Bo, Z. (2023). Geospatial analysis in social media: Exploring Instagram and Twitter activity using geo-visual analytics. GeoJournal of Data Science.
  6. Freire, T., Barbosa, C., & Lopes, F. (2023). Evaluating brand perception through Facebook interactions using SNA. Social Media Intelligence Journal.
  7. Gupta, A. (2025). Predicting consumer behavior using machine learning on social media platforms. AI & Society.
  8. Hossain, M., Sarker, F., & Islam, S. (2024). Impact of data-driven strategies on business performance: An empirical study. Journal of Information and Management Studies.
  9. Jain, S., Kapoor, A., & Gupta, M. (2023). Dynamic pricing models for e-commerce using real-time social media feedback. E-Commerce Analytics Journal.
  10. Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2023). Data-to-decision models in SMA: A framework for performance evaluation. Journal of Business Analytics.
  11. Joshi, N., Roy, D., & Mehra, K. (2025). AI-powered sentiment mining for marketing decision-making. International Journal of Data Science and Marketing.
  12. Kaur, J., & Dhillon, H. (2023). Social media analytics for SMEs: A Twitter-based case study. Journal of Small Business Strategy.
  13. Kumar, V., Sharma, S., & Patel, R. (2023). Assessing brand image through YouTube comments using topic modeling. Journal of Computational Marketing.
  14. Mangal, P., Srivastava, R., & Desai, A. (2024). AI and BI convergence in social media analytics: Enhancing business agility. Business Intelligence Research Journal.
  15. Mahony, T. (2020). Exploring regional small business engagement with Facebook technology. https://consensus.app/papers/exploring-regional-small-business-engagement-with-mahony/124d8603b6665aec9af711a3eb4c08a6
  16. Malhotra, R., Singh, D., & Batra, P. (2023). Scalable SMA pipelines using Hadoop for enterprise campaign analytics. International Journal of Big Data Applications.
  17. Mishra, V., Agarwal, S., & Rao, A. (2023). Sentiment classification using hybrid LSTM-CNN deep learning models. Journal of AI and Customer Analytics.
  18. Mulla, K. (2024). Big data and predictive analytics: SMA applications in business intelligence. International Journal of Business Strategy and Analytics.
  19. Narayan, V., Sen, D., & Dey, P. (2023). Airline pricing optimization using Twitter-based SMA. Journal of Aviation Management & Strategy.
  20. Pathak, A., & Rajput, V. (2023). Geo-visual sentiment clustering for localized marketing campaigns. Marketing Intelligence & Planning.
  21. Reddy, M., & Nair, S. (2023). Semi-supervised sentiment analysis of social media streams in emerging markets. Journal of Information Systems and Emerging Technologies.
  22. Sabah, M., Al-Hadhrami, T., & Ismail, R. (2023). SMDA-DL: A deep learning model for customer sentiment analysis. International Journal of Neural Computing and Applications.
  23. Sachdeva, R., Gupta, L., & Rao, S. (2023). Social media analytics in MIS: Enhancing brand monitoring. Information Systems Journal.
  24. Sauid, K., Mehmood, R., & Khan, A. (2024). SMA-powered competitive benchmarking using big data. Journal of Digital Business Strategy.
  25. Saxena, A., Kaul, R., & Sharma, D. (2023). Online sentiment and customer lifetime value: A brand trust analysis. Customer Insights Journal.
  26. Sharma, I., Verma, G., & Taneja, N. (2023). Multimodal sentiment analysis of Instagram posts. Visual Communication & Marketing Journal.
  27. Sharmin, N., Alam, S., & Chowdhury, H. (2024). Real-time big data tools for social media analytics. International Journal of Information Technology and Decision Making.
  28. Srinidhi, P., Patel, S., & Iyer, A. (2024). Sentiment mining from Twitter using Hadoop: Insights for industry. Journal of Business Data Science.
  29. Thomas, R., Iqbal, A., & George, F. (2023). Cross-sector maturity in SMA adoption: A benchmarking study. Business & Technology Review.
  30. Verma, M., Joshi, R., & Dubey, R. (2023). Aligning promotions with SMA-driven demand forecasting. International Journal of Retail & Distribution Management.
  31. Baghini, A. G. (2015). Small firms and social media contributions: Contributions of using Facebook as a social media in promotion mix for small firms.
  32. Kim, S. (2021). Mapping social media analytics for small business: A case study of business analytics. International Journal of Fashion Design, Technology and Education, 14(2), 218–231. https://doi.org/10.1080/17543266.2021.1915392
  33. Mahony, T. (2020). Exploring regional small business engagement with Facebook technology. https://doi.org/10.25903/ZSRY-NV90
  34. Tzvetkova, D., & Vankov, N. (2019). Web metrics for analysis and optimization of business pages for SMEs through Facebook Insights. In Proceedings on Business and Economics (pp. 265–282).
Index Terms

Computer Science
Information Sciences

Keywords

Social media Artificial intelligence Machine Learning Business Analytics

Powered by PhDFocusTM