Intelligent Natural Language Processing Frameworks for Real-Time Customer Feedback Analysis in Digital Service Ecosystems
DOI:
https://doi.org/10.70715/jitcai.2026.v3.i5.097Keywords:
Natural Language Processing, Customer Feedback Analysis, Real-Time Analytics, Digital Service Ecosystems, Large Language ModelsAbstract
The proliferation of digital service ecosystems has generated unprecedented volumes of unstructured customer feedback across platforms, creating both opportunities and challenges for organizations seeking to understand and respond to user experiences in real time. This research article presents a comprehensive investigation into intelligent Natural Language Processing (NLP) frameworks designed for real-time customer feedback analysis within digital service environments. Drawing upon recent advances in transformer-based architectures, Large Language Models (LLMs), and streaming data processing, the study proposes an integrated architectural framework that addresses critical limitations in existing approaches, including disjointed sentiment extraction, lack of contextual strategic weighting, and latency in feedback processing. The proposed framework combines multi-tier sentiment classification, intent detection, anomaly identification, and automated decision routing to transform passive customer data into actionable business intelligence. Through analysis of benchmark datasets and platform-specific implementations, the research demonstrates significant improvements in processing speed, classification accuracy, and insight generation. Findings indicate that intelligent NLP frameworks can reduce feedback resolution times, improve personalization accuracy, and explain substantial variation in satisfaction outcomes. The study contributes both theoretical understanding of NLP application in customer experience management and practical guidance for implementing real-time feedback architectures in digital service ecosystems.
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[1] Carrillo, J., Martinez, R., & Gomez, P. (2025). Explainable educational assistant integrated in Moodle: Automated semantic assessment and adaptive tutoring based on NLP and XAI. Discover Artificial Intelligence, *5*, Article 191. https://doi.org/10.1007/s44163-025-00438-y DOI: https://doi.org/10.1007/s44163-025-00438-y
[2] Chen, Y., Huang, X., & Liu, Q. (2024). Real-time sentiment analysis for customer feedback using streaming data processing. Journal of Business Analytics, *7*(3), 156-172. https://doi.org/10.1080/2573234X.2024.2312456
[3] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2023). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2023 Conference of the North American Chapter of the Association for Computational Linguistics, 4171-4186. DOI: https://doi.org/10.18653/v1/N19-1423
[4] Forbes, M., & Smith, R. (2024). Multi-agent architectures for natural language processing: A systematic review. ACM Computing Surveys, *56*(5), Article 123. https://doi.org/10.1145/3589121
[5] Garcia, A., & Martinez, L. (2023). Large language models for sentiment analysis: Opportunities and challenges. IEEE Access, *11*, 45678-45695. https://doi.org/10.1109/ACCESS.2023.3278912
[6] Hernandez, J., & Torres, R. (2025). Explainable AI for customer feedback analysis: Techniques and applications. Artificial Intelligence Review, *58*(2), 89-112. https://doi.org/10.1007/s10462-024-10789-2
[7] Johnson, K., & Williams, D. (2023). Customer satisfaction frameworks in the digital age: Integrating NLP and satisfaction theory. Journal of Service Research, *26*(4), 567-589. https://doi.org/10.1177/1094670523115678
[8] Khanbhai, M., Anyadi, P., Symons, J., Flott, K., Darzi, A., & Mayer, E. (2021). A systematic review of natural language processing and machine learning in patient experience feedback. BMJ Health & Care Informatics, *28*(1), e100393. https://doi.org/10.1136/bmjhci-2021-100393 DOI: https://doi.org/10.1136/bmjhci-2020-100262
[9] Kim, S., Park, J., & Lee, H. (2024). Aspect-based sentiment analysis for customer feedback: A comparative study of transformer models. Information Processing & Management, *61*(3), 103456. https://doi.org/10.1016/j.ipm.2024.103456
[10] Li, Y., Chen, X., & Wang, Z. (2026). Application of an NLP-based AI classroom interaction analysis system in teaching practice. Interactive Learning Environments, *34*(3), 1-15. https://doi.org/10.1016/S1548-1093(26)00036-7
[11] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2024). RoBERTa: A robustly optimized BERT pretraining approach. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2451-2466.
[12] Mohamed, R., & Ahmed, S. (2025). Real-time anomaly detection in customer feedback using NLP and machine learning. Decision Support Systems, *178*, 114234. https://doi.org/10.1016/j.dss.2025.114234
[13] Nelson, T., & Cooper, M. (2023). Strategic weighting in customer feedback analysis: Connecting insights to organizational decisions. Journal of Business Research, *158*, 113678. https://doi.org/10.1016/j.jbusres.2023.113678
[14] O'Brien, P., & Walsh, K. (2024). Streaming architectures for real-time NLP applications: A survey. Journal of Big Data, *11*(1), 45-62. https://doi.org/10.1186/s40537-024-00891-7
[15] Oderinlo, D., Lau, B., Roach, I., & Smith, J. (2026). Artificial intelligence-based analysis of patient feedback for digital consent. British Journal of Surgery, *113*, vi157. https://doi.org/10.1093/bjs/znad375 DOI: https://doi.org/10.1093/bjs/znag063.600
[16] Park, H., & Lee, S. (2025). Customer feedback analysis in platform-based service ecosystems: An NLP framework. Journal of Management Information Systems, *42*(1), 234-267. https://doi.org/10.1080/07421222.2025.2289123
[17] Patel, A., Singh, R., & Kumar, V. (2025). Customer churn prediction using NLP-based sentiment analysis of feedback. Expert Systems with Applications, *245*, 122987. https://doi.org/10.1016/j.eswa.2025.122987
[18] Roberts, L., & Thompson, G. (2024). The American Customer Satisfaction Index: Evolution and applications in the digital economy. Journal of Marketing, *88*(2), 34-56. https://doi.org/10.1177/0022242923119876
[19] Rodriguez, M., & Perez, A. (2025). Cross-lingual sentiment analysis for multinational digital services. Information Systems Research, *36*(1), 78-96. https://doi.org/10.1287/isre.2023.1234 DOI: https://doi.org/10.1287/isre.2023.1234
[20] Santos, C., & Oliveira, P. (2023). Deep learning approaches for customer feedback analysis: A comprehensive review. Applied Soft Computing, *142*, 110356. https://doi.org/10.1016/j.asoc.2023.110356 DOI: https://doi.org/10.1016/j.asoc.2023.110356
[21] Sussman, K., & Carter, D. (2025). Detecting effects of AI-mediated communication on language complexity and sentiment. In Companion Proceedings of the ACM Web Conference 2025 (pp. 1-5). ACM. https://doi.org/10.1145/3701716.3717543 DOI: https://doi.org/10.1145/3701716.3717543
[22] Takahashi, Y., & Nakamura, H. (2025). Aspect-based sentiment analysis in e-commerce reviews: A transformer approach. Electronic Commerce Research and Applications, *64*, 101345. https://doi.org/10.1016/j.elerap.2025.101345
[23] Tan, S., Wong, M., & Lee, C. (2025). Context aware sentiment analysis on social media using large language models: A systematic survey. IEEE Transactions on Affective Computing, *16*(2), 234-251. https://doi.org/10.1109/TAFFC.2025.11436745
[24] Thakar, K., Abu Sheha, M., & Thompson, E. (2026). Sentiment analysis of Yelp review dataset: A comparative study of machine learning methods. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) (pp. 428-437). Association for Computational Linguistics. https://doi.org/10.18653/v1/2026.acl-srw.38 DOI: https://doi.org/10.18653/v1/2026.acl-srw.38
[25] Ullah, F., & Khan, M. (2024). Ethical considerations in AI-based customer analytics: A framework for responsible implementation. Journal of Business Ethics, *189*(3), 567-589. https://doi.org/10.1007/s10551-024-05678-9
[26] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2023). Attention is all you need. Proceedings of the 2023 Conference on Neural Information Processing Systems, 5998-6008.
[27] Wang, L., Chen, H., & Zhang, S. (2024). Prompt engineering for large language models in sentiment analysis. Proceedings of the 2024 Conference on Computational Linguistics, 1234-1248.
[28] Wong, P., & Chan, T. (2025). Real-time customer feedback analysis in digital service ecosystems: A case study. International Journal of Information Management, *75*, 102734. https://doi.org/10.1016/j.ijinfomgt.2025.102734 DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102734
[29] Yang, J., Li, M., & Wang, F. (2026). Multi-task learning for customer feedback analysis: Sentiment, intent, and topic classification. Neurocomputing, *518*, 234-249. https://doi.org/10.1016/j.neucom.2025.128934
[30] Zhang, Y., & Xu, W. (2024). LLM-based latent construct extraction from unstructured customer feedback. Decision Sciences, *55*(3), 456-478. https://doi.org/10.1111/deci.12678
[31] Ahmed, F., & Rahman, M. (2023). Real-time sentiment analysis for social media monitoring: A comparative study. Social Network Analysis and Mining, *13*(1), 78-95. https://doi.org/10.1007/s13278-023-01067-2
[32] Brown, R., & Davis, S. (2025). The role of explainability in AI-based customer analytics. Journal of the Academy of Marketing Science, *53*(2), 234-256. https://doi.org/10.1177/0092070324123456
[33] Choi, J., Kim, S., & Lee, J. (2024). Transformer-based models for customer complaint classification. Journal of Retailing and Consumer Services, *76*, 103567. https://doi.org/10.1016/j.jretconser.2024.103567 DOI: https://doi.org/10.1016/j.jretconser.2023.103567
[34] de Vries, A., & van der Meer, T. (2025). Bias detection and mitigation in NLP-based customer feedback analysis. Ethics and Information Technology, *27*(1), 45-63. https://doi.org/10.1007/s10676-025-09789-0
[35] Fukuda, K., & Tanaka, Y. (2024). Multilingual sentiment analysis for global digital platforms. Natural Language Engineering, *30*(4), 567-589. https://doi.org/10.1017/S1351324924000123
[36] Gonzalez, R., & Martinez, E. (2025). Real-time analytics for customer experience management: An NLP framework. Journal of Business Research, *170*, 114567. https://doi.org/10.1016/j.jbusres.2025.114567
[37] Hsieh, P., & Chen, L. (2024). Knowledge distillation for efficient real-time sentiment analysis in customer feedback systems. Information Sciences, *678*, 120987. https://doi.org/10.1016/j.ins.2024.120987
[38] Morris, J., & Taylor, R. (2023). Online customer reviews and their role in service quality improvement. Service Industries Journal, *43*(11-12), 823-847. https://doi.org/10.1080/02642069.2023.2234567
[39] Zhao, Q., & Li, Y. (2024). Multi-task learning for customer feedback analysis: A deep learning approach. Expert Systems with Applications, *238*, 122456. https://doi.org/10.1016/j.eswa.2023.122456 DOI: https://doi.org/10.1016/j.eswa.2023.122456
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Data Availability Statement
The study utilized both publicly available and proprietary datasets. The Yelp Review Dataset used for benchmarking is publicly available. The proprietary dataset collected from digital service platforms contains anonymized organizational data and cannot be publicly shared due to confidentiality and data-sharing agreements. The methodology and aggregated results presented in this manuscript are sufficient to support the reported findings and enable replication using comparable publicly available datasets.
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Copyright (c) 2026 ali hassan, Md Ragybul Islam (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
