Cross-Platform Sentiment Analytics for Unified Customer Feedback in Digital Business Environments
DOI:
https://doi.org/10.32628/IJSRST25123133Keywords:
cross-platform analytics, sentiment analysis, customer feedback, digital business, data fusion, NLP, domain adaptationAbstract
This paper explores cross-platform sentiment analytics to unify customer feedback in digital business environments, enabling organizations to gain actionable insights from diverse data sources such as social media, reviews, and surveys. A systematic literature review synthesizes insights on sentiment analysis techniques, cross-platform data integration, and their applications in customer experience management. A proposed framework integrates these elements into a cohesive system for real-time feedback analysis. Application scenarios in retail, hospitality, and financial services illustrate the framework’s potential, suggesting up to 30% improvement in customer satisfaction and 25% increase in retention rates. The study contributes to the literature on digital business intelligence and offers practical guidelines for leveraging sentiment analytics to enhance customer-centric strategies.
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Z. Van Veldhoven and J. Vanthienen, “Digital transformation as an interaction-driven perspective between business, society, and technology,” Electronic Markets, vol. 32, no. 2, pp. 629–644, Jun. 2022, doi: 10.1007/S12525-021-00464-5/METRICS.
N. J. Isibor, C. Paul-Mikki Ewim, A. I. Ibeh, E. M. Adaga, N. J. Sam-Bulya, and G. O. Achumie, “A Generalizable Social Media Utilization Framework for Entrepreneurs: Enhancing Digital Branding, Customer Engagement, and Growth,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2, no. 1, pp. 751–758, 2021, doi: 10.54660/.IJMRGE.2021.2.1.751-758.
N. J. Isibor, C. P. M. Ewim, A. I. Ibeh, E. M. Adaga, N. J. Sam-Bulya, and G. O. Achumie, “A generalizable social media utilization framework for entrepreneurs: Enhancing digital branding, customer engagement, and growth,” International Journal of Multidisciplinary Research and Growth Evaluation, 2021.
E. C. Chianumba, N. Ikhalea, A. Y. Mustapha, A. Y. Forkuo, and D. Osamika, “Integrating AI, blockchain, and big data to strengthen healthcare data security, privacy, and patient outcomes,” researchgate.netEC Chianumba, N Ikhalea, AY Mustapha, AY Forkuo, D OsamikaJournal of Frontiers in Multidisciplinary Research, 2022•researchgate.net, vol. 3, no. 1, pp. 124–129, 2022, doi: 10.54660/.IJFMR.2022.3.1.124-129.
B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, “Machine learning for automation: Developing data-driven solutions for process optimization and accuracy improvement,” Mach Learn, vol. 2, no. 1, 2021.
E. C. Chukwuma-Eke, O. Y. Ogunsola, and N. J. Isibor, “Designing a robust cost allocation framework for energy corporations using SAP for improved financial performance,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2, 2021.
Ezeh, A. F. S., U. O. S., L. U. S., C. I. Friday, and S. C, “Systematic review of user experience optimization in multi-channel digital payment platform design,” S., Adanigbo, O. S., Ugbaja, U. S., Lawal, C. I., & Friday, S. C. (2023). Systematic review of user experience optimization in multi-channel digital payment platform design. Gulf Journal of Advance Business Research, vol. 2023), 2023, [Online]. Available: https://doi.org/10.51594/gjabr.v1i3.135
Adanigbo, E. O. S., U. F. S., L. U. S., C. I. Friday, and S. C, “A conceptual framework for risk mitigation and operational efficiency in treasury payment systems,” S., Ezeh, F. S., Ugbaja, U. S., Lawal, C. I., & Friday, S. C. (2023). A conceptual framework for risk mitigation and operational efficiency in treasury payment systems. Gulf Journal of Advance Business Research, vol. 2023), 2023, [Online]. Available: https://doi.org/10.51594/gjabr.v1i3.134
Ajiga and D. I, “Strategic framework for leveraging artificial intelligence to improve financial reporting accuracy and restore public trust,” I. (2021). Strategic framework for leveraging artificial intelligence to improve financial reporting accuracy and restore public trust. International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2021), 2021.
Esan, U. O. J., O. O. T., O. O., G. O. Etukudoh, and E. A, “Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework,” J., Uzozie, O. T., Onaghinor, O., Osho, G. O., & Etukudoh, E. A. (2022). Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework. Journal of Frontiers in Multidisciplinary Research, vol. 2022), 2022.
Esan, U. O. J., O. O. T., O. O., G. O. Omisola, and J. O, “Policy and operational synergies: Strategic supply chain optimization for national economic growth,” J., Uzozie, O. T., Onaghinor, O., Osho, G. O., & Omisola, J. O. (2022). Policy and operational synergies: Strategic supply chain optimization for national economic growth. International Journal of Social Science Exceptional Research, vol. 2022), 2022.
S. D. M. Biliyamin Adeoye Ibitoye and R. AbdulWahab, “Estimation of Drivers’ Critical Gap Acceptance and Follow-up Time at Four – Legged Unsignalized Intersection,” CARD International Journal of Science and Advanced Innovative Research, vol. 1, no. 1, 2017.
N. Rane, M. Paramesha, S. Choudhary, and J. Rane, “Artificial Intelligence in Sales and Marketing: Enhancing Customer Satisfaction, Experience and Loyalty,” SSRN Electronic Journal, May 2024, doi: 10.2139/SSRN.4831903.
Esan, U. O. J., O. O. T., O. O., G. O. Etukudoh, and E. A, “Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework,” J., Uzozie, O. T., Onaghinor, O., Osho, G. O., & Etukudoh, E. A. (2022). Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework. Journal of Frontiers in Multidisciplinary Research, vol. 2022), 2022, [Online]. Available: https://doi.org/10.51594/estj.v6i2.1862
V. Braun and V. Clarke, “Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches,” Couns Psychother Res, vol. 21, no. 1, pp. 37–47, Mar. 2021, doi: 10.1002/CAPR.12360;PAGE:STRING:ARTICLE/CHAPTER.
hl=en Cynthia Ozobu https://scholar.google.com/citations?view_op=list_works hl=en user=B8jjbqwAAAAJ sortby=title, “Unknown Title,” vol. 8, [Online]. Available: https://scholar.google.com/citations?view_op=list_works&hl=en&hl=en&user=B8jjbqwAAAAJ&sortby=title
M. Holmlund et al., “Customer experience management in the age of big data analytics: A strategic framework,” J Bus Res, vol. 116, pp. 356–365, Aug. 2020, doi: 10.1016/J.JBUSRES.2020.01.022.
Esan, U. O. J., O. O. T., O. O., G. O. Etukudoh, and E. A, “Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework,” J., Uzozie, O. T., Onaghinor, O., Osho, G. O., & Etukudoh, E. A. (2022). Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework. Journal of Frontiers in Multidisciplinary Research, vol. 2022), 2022.
S. S. Roy, R. Chopra, K. C. Lee, C. Spampinato, and B. Mohammadi-Ivatlood, “Random forest, gradient boosted machines and deep neural network for stock price forecasting: A comparative analysis on South Korean companies,” International Journal of Ad Hoc and Ubiquitous Computing, vol. 33, no. 1, pp. 62–71, 2020, doi: 10.1504/IJAHUC.2020.104715;PAGE:STRING:ARTICLE/CHAPTER.
M. Barika, S. Garg, A. Y. Zomaya, L. Wang, A. V. A. N. Moorsel, and R. Ranjan, “Orchestrating big data analysis workflows in the cloud: Research challenges, survey, and future directions,” ACM Comput Surv, vol. 52, no. 5, p. 95, Sep. 2019, doi: 10.1145/3332301/SUPPL_FILE/BARIKA.ZIP.
W. Elugbaju, O. Alabi, W. Kasope Elugbaju, N. I. Okeke, and O. A. Alabi, “Human Resource Analytics as a Strategic Tool for Workforce Planning and Succession Management,” 2024. [Online]. Available: www.ijerd.com
Wande Kasope Elugbaju, Nnenna Ijeoma Okeke, and Olufunke Anne Alabi, “Conceptual framework for enhancing decision-making in higher education through data-driven governance,” Global Journal of Advanced Research and Reviews, vol. 2, no. 2, pp. 016–030, Oct. 2024, doi: 10.58175/gjarr.2024.2.2.0055.
Olufunke Anne Alabi, Funmilayo Aribidesi Ajayi, Chioma Ann Udeh, and Christianah Pelumi Efunniyi, “Data-driven employee engagement: A pathway to superior customer service,” World Journal of Advanced Research and Reviews, vol. 23, no. 3, pp. 923–933, Sep. 2024, doi: 10.30574/wjarr.2024.23.3.2733.
A. S. Ogunmokun, E. D. Balogun, and K. O. Ogunsola, “A Conceptual Framework for AI-Driven Financial Risk Management and Corporate Governance Optimization,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2, 2021.
M. K. Gargari, “Improving Customer Experience Management: A Dynamic Topic Modelling Approach,” 2023.
hl=en David Ajiga ;https://scholar.google.com/citations?user=zC5wizQAAAAJ oi=ao, “Unknown Title,” vol. 5, [Online]. Available: https://scholar.google.com/citations?user=zC5wizQAAAAJ&hl=en&oi=ao
R. Maria, “The use of theory and methods of behavioural economics in the process of making financial decisions,” Review of Business and Economics Studies, vol. 7, no. 3, pp. 45–82, Sep. 2019, doi: 10.26794/2308-944X-2019-7-3-45-82.
J. O. Ojadi, E. C. Onukwulu, C. S. Odionu, and O. A. Owulade, “Natural Language Processing for Climate Change Policy Analysis and Public Sentiment Prediction: A Data-Driven Approach to Sustainable Decision-Making,” IRE Journals, vol. 7, no. 3, pp. 731–749, 2023, [Online]. Available: https://www.irejournals.com
L. Stark and J. Hoey, “The ethics of emotion in artificial intelligence systems,” FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 782–793, Mar. 2021, doi: 10.1145/3442188.3445939;JOURNAL:JOURNAL:ACMCONFERENCES;PAGEGROUP:STRING:PUBLICATION.
A. G. Olabi, T. Wilberforce, K. Elsaid, E. T. Sayed, H. M. Maghrabie, and M. A. Abdelkareem, “Large scale application of carbon capture to process industries – A review,” J Clean Prod, vol. 362, p. 132300, Aug. 2022, doi: 10.1016/J.JCLEPRO.2022.132300.
D. Di Battista, F. Fatigati, R. Carapellucci, and R. Cipollone, “An improvement to waste heat recovery in internal combustion engines via combined technologies,” Energy Convers Manag, vol. 232, p. 113880, Mar. 2021, doi: 10.1016/J.ENCONMAN.2021.113880.
E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, and E. D. Balogun, “Real-time data analytics for enhancing supply chain efficiency,” Journal of Supply Chain Management and Analytics, vol. 10, no. 1, pp. 49–60, 2023.
E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, E. D. Balogun, and K. O. Ogunsola, “Real-time data analytics for enhancing supply chain efficiency,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2, no. 1, pp. 759–771, 2021, doi: 10.54660/.IJMRGE.2021.2.1.759-771.
I. A. Omar, R. Jayaraman, M. S. Debe, K. Salah, I. Yaqoob, and M. Omar, “Automating Procurement Contracts in the Healthcare Supply Chain Using Blockchain Smart Contracts,” IEEE Access, vol. 9, pp. 37397–37409, 2021, doi: 10.1109/ACCESS.2021.3062471.
E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, and E. D. Balogun, “Enhancing data security with machine learning: A study on fraud detection algorithms,” Journal of Data Security and Fraud Prevention, vol. 7, no. 2, pp. 105–118, 2021.
Adelusi, O. B. S., K.-A. D., M. M. C., A. Y. Ikhalea, and N, “A data-driven framework for early detection and prevention of non-communicable diseases in healthcare systems,” S., Osamika, D., Kelvin-Agwu, M. C., Mustapha, A. Y., & Ikhalea, N. (2024). A data-driven framework for early detection and prevention of non-communicable diseases in healthcare systems. IRE Journals, vol. 2024), 2024.
I. Herath, “Cross-Platform Development With Full-Stack Frameworks: Bridging the Gap for Seamless Integration,” 2024, Accessed: May 11, 2025. [Online]. Available: http://www.theseus.fi/handle/10024/867532
E. D. Balogun, K. O. Ogunsola, and A. Samuel, “A cloud-based data warehousing framework for real-time business intelligence and decision-making optimization,” International Journal of Business Intelligence Frameworks, vol. 6, no. 4, pp. 121–134, 2021.
F. O. Onyeke, W. N. Digitemie, M. A. Adewoyin, and I. N. Dienagha, “Design Thinking for SaaS Product Development in Energy and Technology: Aligning User-Centric Solutions with Dynamic Market Demands,” Iconic Research and Engineering Journals, vol. 6, no. 8, pp. 316–323, 2023.
O. Ilori, C. I. Lawal, S. C. Friday, N. J. Isibor, and E. C. Chukwuma-Eke, “The Role of Data Visualization and Forensic Technology in Enhancing Audit Effectiveness: A Research Synthesis,” 2022.
B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, “Machine learning for automation: Developing data-driven solutions for process optimization and accuracy improvement,” Mach Learn, vol. 2, no. 1, p. 18, 2021.
M. N. Fekri, K. Grolinger, and S. Mir, “Distributed load forecasting using smart meter data: Federated learning with Recurrent Neural Networks,” International Journal of Electrical Power & Energy Systems, vol. 137, p. 107669, May 2022, doi: 10.1016/J.IJEPES.2021.107669.
A. Abisoye and J. I. Akerele, “A High-Impact Data-Driven Decision-Making Model for Integrating Cutting-Edge Cybersecurity Strategies into Public Policy, Governance, and Organizational Frameworks,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2, no. 1, pp. 623–637, 2021, doi: 10.54660/.IJMRGE.2021.2.1.623-637.
D. Nyangoma, E. M. Adaga, N. J. Sam-Bulya, and G. O. Achumie, “Integrating sustainability principles into agribusiness operations: A strategic framework for environmental and economic viability,” International Journal of Management and Organizational Research, vol. 2, no. 1, pp. 288–295, 2023, doi: 10.54660/IJMOR.2023.2.1.288-295.
D. Nyangoma, E. M. Adaga, N. J. Sam-Bulya, and G. O. Achumie, “A sustainable agribusiness workforce development model: Bridging agricultural innovation and economic empowerment,” International Journal of Management and Organizational Research, vol. 2, no. 1, pp. 274–280, 2023, doi: 10.54660/IJMOR.2023.2.1.274-280.
Z. Xue, N. S. M. Nasir, Y. Cheng, W. Wu, and Y. Cao, “OVERCOMING RESISTANCE TO INNOVATION: STRATEGIES AND CHANGE MANAGEMENT,” Journal of Business Innovation, vol. 9, no. 1, p. 32, Nov. 2024, Accessed: May 11, 2025. [Online]. Available: https://www.unimel.edu.my/journal/index.php/JBI/article/view/1789
“Advances in global services and retail management: Volume 2,” Advances in global services and retail management: Volume 2, Sep. 2021, doi: 10.5038/9781955833035.
R. Damaševičius, N. Bacanin, and S. Misra, “From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management,” Journal of Sensor and Actuator Networks 2023, Vol. 12, Page 41, vol. 12, no. 3, p. 41, May 2023, doi: 10.3390/JSAN12030041.
F. Halper, “Advanced Analytics: Moving Toward AI, Machine Learning, and Natural Language Processing BEST PRACTICES REPORT,” 2017.
C. I. Okolie, O. Hamza, A. Eweje, A. Collins, G. O. Babatunde, and B. C. Ubamadu, “Implementing Robotic Process Automation (RPA) to Streamline Business Processes and Improve Operational Efficiency in Enterprises,” International Journal of Social Science Exceptional Research, vol. 1, no. 1, pp. 111–119, 2022, doi: 10.54660/IJMRGE.2022.1.1.111-119.
R. Narayan and R. Narayan, “Computational Linguistic Features of Code-switching ‎Amongst Native Fiji-Hindi Speakers on Facebook,” Journal of Applied Linguistics and Language Research, vol. 7, no. 1, pp. 19–45, Feb. 2020, Accessed: May 15, 2025. [Online]. Available: https://jallr.com/~jallrir/index.php/JALLR/article/view/1075
S. Lee, “A Study on the Changing Architectural Properties of Mixed-Use Commercial Complexes in Seoul, Korea,” Sustainability 2022, Vol. 14, Page 2649, vol. 14, no. 5, p. 2649, Feb. 2022, doi: 10.3390/SU14052649.
Ajiga and D. I, “Strategic framework for leveraging artificial intelligence to improve financial reporting accuracy and restore public trust,” I. (2021). Strategic framework for leveraging artificial intelligence to improve financial reporting accuracy and restore public trust. International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2021), 2021.
O. Awoyemi, R. Uchenna Attah, J. Ozigi Basiru, and I. M. Leghemo, “A Technology Integration Blueprint for Overcoming Digital Literacy Barriers in Developing World Educational Systems,” 2023.
A. Abisoye and J. I. Akerele, “A Practical Framework for Advancing Cybersecurity, Artificial Intelligence and Technological Ecosystems to Support Regional Economic Development and Innovation,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 3, no. 1, pp. 700–713, 2022, doi: 10.54660/.IJMRGE.2022.3.1.700-713.
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