Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sangunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.
This study presents a user-driven, emotion-aware expert system designed for intelligent consumer targeting within man–machine computing environments. Traditional digital marketing frameworks rely heavily on generalized behavioral analytics, lacking real-time emotional awareness and failing to capture nuanced user intent. To address these limitations, we propose a next-generation AI architecture that integrates multimodal emotion detection—including facial expression analysis, vocal tone interpretation, and textual sentiment mining—into the targeting process. The system employs a hybrid deep learning framework combining Convolutional Neural Networks (CNN) for visual emotion recognition and Bi-directional Long Short-Term Memory (Bi-LSTM) for sequential audio-text analysis, enhanced by a dynamic attention mechanism. Implemented within a modular, Python-based platform, this expert system enables seamless integration with existing digital marketing ecosystems and supports real-time data processing. Experimental evaluations demonstrate a 21.6% improvement in targeting accuracy over behavior-only models and a 92.4% emotion recognition rate on standard benchmarks. Results show increased user engagement, improved personalization, and higher campaign effectiveness. This research contributes to the field of augmented intelligence and expert systems by advancing man–machine interaction and enabling emotionally adaptive consumer profiling for smarter, human-centered digital marketing strategies.
C. Ziakis and M. Vlachopoulou, “Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review,” Information, vol. 14, no. 12, p. 664, Dec. 2023, doi: 10.3390/info14120664.
I. Antosova, M. Psurny, and J. Stavkova, “Changes in Consumer Purchasing Decisions: Traditional and Emerging Factors in the Dynamic Marketing Landscape Over 15 Years,” Marketing and Management of Innovations, vol. 14, no. 3, pp. 85–96, 2023, doi: 10.21272/mmi.2023.3-08.
J. Dong, G. Li, W. Ma, and J. Liu, “Personalized recommendation system based on social tags in the era of Internet of Things,” Journal of Intelligent Systems, vol. 31, no. 1, pp. 681–689, Jan. 2022, doi: 10.1515/jisys-2022-0053.
L. M. Pedrero-Esteban and A. Barrios-Rubio, “Digital Communication in the Age of Immediacy,” Digital, vol. 4, no. 2, pp. 302–315, Mar. 2024, doi: 10.3390/digital4020015.
D. K. Baringer and J. C. McCroskey, “Immediacy in the classroom: Student immediacy,” Communication Education, vol. 49, no. 2, pp. 178–186, Apr. 2000, doi: 10.1080/03634520009379204.
Dandridge, “Fitting business models in sociotechnical transitions: the marketing of utility energy efficiency programs in Canada and the US,” 2022.
M. Ali, “Personalized digital marketing strategy, corporate image, and customer loyalty in the telecommunication sector in Kenya.,” PhD Thesis, Cuk, 2024.
I. Journal, “Influence Of Social Media On Consumer Buying Behavior,” Interantional Journal Of Scientific Research In Engineering And Management, Vol. 07, No. 08, Aug. 2023, Doi: 10.55041/Ijsrem25382.
Z. Ma, Q. Gao, and Y. Chen, “Post-purchase trust in e-commerce: A theoretical framework and a text mining-based assessment method,” International Journal of Human–Computer Interaction, vol. 39, no. 8, pp. 1643–1661, 2023.
A. T. Rosário and J. C. Dias, “How has data-driven marketing evolved: Challenges and opportunities with emerging technologies,” International Journal of Information Management Data Insights, vol. 3, no. 2, p. 100203, Nov. 2023, doi: 10.1016/j.jjimei.2023.100203.
D. Andayani, M. Madani, H. Agustian, N. Septiani, and L. W. Ming, “Optimizing Digital Marketing Strategies through Big Data and Machine Learning: Insights and Applications,” Journal of Computer Science and Technology Application, vol. 1, no. 2, pp. 104–110, 2024.
J. M. Valdez Mendia and J. J. A. Flores-Cuautle, “Toward customer hyper-personalization experience — A data-driven approach,” Cogent Business & Management, vol. 9, no. 1, Feb. 2022, doi: 10.1080/23311975.2022.2041384.
S. Bozkurt, A. S. Uğursoy, and S. P. Meral, “The Impact of Personalized Messages and Designs on Consumer Experiences and Marketing Communications in Technology,” Impact of Contemporary Technology on Art and Design, pp. 135–164, Oct. 2024, doi: 10.4018/979-8-3693-4318-0.ch007.
Raiyan Haider, Md Farhan Abrar Ibne Bari, Md. Farhan Israk Shaif, and Mushfiqur Rahman, “Engineering hyper-personalization: Software challenges and brand performance in AI-driven digital marketing management: An empirical study,” International Journal of Science and Research Archive, vol. 15, no. 2, pp. 1122–1141, May 2025, doi: 10.30574/ijsra.2025.15.2.1525.
A. Aliev and D. Kadirov, “Digital Marketing and Smart Technology Marketing Systems as the Future of Metaverse,” Internet of Things, Smart Spaces, and Next Generation Networks and Systems, pp. 397–410, 2023, doi: 10.1007/978-3-031-30258-9_35.
C. Valor, P. Antonetti, and B. Crisafulli, “Emotions and consumers’ adoption of innovations: An integrative review and research agenda,” Technological Forecasting and Social Change, vol. 179, p. 121609, Jun. 2022, doi: 10.1016/j.techfore.2022.121609.
D. Juárez-Varón, A. Mengual-Recuerda, A. Capatina, and M. Núñez Cansado, “Footwear consumer behavior: The influence of stimuli on emotions and decision making,” Journal of Business Research, vol. 164, p. 114016, Sep. 2023, doi: 10.1016/j.jbusres.2023.114016.
A. Haris, “Digital Marketing and Consumer Engagement: A Systematic Review,” Amkop Management Accounting Review (AMAR), vol. 3, no. 2, pp. 75–89, Dec. 2023, doi: 10.37531/amar.v3i2.1442.
J. K. Reddy, “Leveraging Generative AI for Hyper Personalized Rewards and Benefits Programs: Analyzing Consumer Behavior in Financial Loyalty Systems,” J. Electrical Systems, vol. 20, no. 11s, pp. 3647–3657, 2024.
M. Alabi, “Data-Driven User Experience (UX) Design: Creating Personalized Experiences,” 2023.
A. A. Bozdağ, “The AI-mediated intimacy economy: a paradigm shift in digital interactions,” AI & SOCIETY, vol. 40, no. 4, pp. 2285–2306, Dec. 2024, doi: 10.1007/s00146-024-02132-6.
J. Femi and A. Olaoluwa, “The Rise of Emotion-Aware AI: Combating Social Isolation in the US Adult Population,” 2024.
G. Gamage, D. De Silva, N. Mills, D. Alahakoon, and M. Manic, “Emotion AWARE: an artificial intelligence framework for adaptable, robust, explainable, and multi-granular emotion analysis,” Journal of Big Data, vol. 11, no. 1, Jul. 2024, doi: 10.1186/s40537-024-00953-2.
P. Saini, M. N. Choudhary, J. Jacob, and others, “Emotion-Aware Artificial Intelligence: Enhancing Human-Machine,” PRATIBODH, no. NCDSNS, 2023.
J. Z. Wang et al., “Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion,” Proceedings of the IEEE, vol. 111, no. 10, pp. 1236–1286, Oct. 2023, doi: 10.1109/jproc.2023.3273517.
X. Chen et al., “Emotion-aware Design in Automobiles: Embracing Technology Advancements to Enhance Human-vehicle Interaction,” Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pp. 1–18, Apr. 2025, doi: 10.1145/3706598.3713571.
G. Tjioe, B. M. Wijaya, J. F. Evarianto, V. A. Darmawan, and E. Erwin, “Conquering Customer Hearts : Intelligent Hyper-Personalization With Artificial Intelligence For Enhanced Customer Engagement,” Jurnal Ilmiah Manajemen, Ekonomi, & Akuntansi (MEA), vol. 9, no. 1, pp. 3336–3356, Apr. 2025, doi: 10.31955/mea.v9i1.5589.
B. Singh and M. M. Ahmed, “Revolutionizing digital marketing: the impact of artificial intelligence on personalized campaigns,” International Research Journal of Business and Social Science, vol. 10, 2024.
V. Gujar, “New Age Marketing: AI Personalization Strategies In Digital World,” IARJSET, vol. 11, no. 3, Mar. 2024, doi: 10.17148/iarjset.2024.11346.
V. Soni, “A Meta-Analytic Examination of the Effects of Personalized Digital Marketing on Consumer Purchasing,” International Journal of Computer Trends and Technology, vol. 72, no. 9, pp. 32–43, Sep. 2024, doi: 10.14445/22312803/ijctt-v72i9p106.
Saxena and S. M. Muneeb, “Transforming Financial Services Through Hyper-Personalization: The Role of Artificial Intelligence and Data Analytics in Enhancing Customer Experience,” in AI-Driven Decentralized Finance and the Future of Finance, IGI Global, 2024, pp. 19–47.
E. T. P. Saratian, M. Efendi, and M. P. Utami, “Emotional Intelligence in Marketing: Connecting Brands with Consumers,” Jurnal Informatika Ekonomi Bisnis, pp. 471–475, Jun. 2024, doi: 10.37034/infeb.v6i2.897.
M. Abdelsalam Mohamed Mostafa, “Digital Marketing -Navigating the Modern Landscape with Artificial Intelligence:<i> From Core Principles to AI-Driven Transformation</i>,” 2025, doi: 10.2139/ssrn.5278617.
Q. Abbas, “The impact of personalization strategies on consumer engagement and conversion rates in digital marketing,” International Journal of Advanced Multidisciplinary Research and Study, vol. 4, no. 1, pp. 452–454, 2024.
Dunsin, “Ai-Driven Personalisation In E-Commerce: Enhancing Consumer Experience And Loyalty”.
Z. I. A. Hakim, “Multimodal EmotionLines Dataset(MELD) A dataset for Emotion Recognition in Multiparty Conversations.” 2021. [Online]. Available: https://www.kaggle.com/datasets/zaber666/meld-dataset?utm_source=chatgpt.com
W. Ai, F. Zhang, Y. Shou, T. Meng, H. Chen, and K. Li, “Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 11, pp. 11418–11426, Apr. 2025, doi: 10.1609/aaai.v39i11.33242.
H. F. T. Alsaadawı and R. Daş, “Multimodal Emotion Recognition Using Bi-LG-GCN for MELD Dataset,” Balkan Journal of Electrical and Computer Engineering, vol. 12, no. 1, pp. 36–46, Mar. 2024, doi: 10.17694/bajece.1372107.
T. Meng, F. Zhang, Y. Shou, W. Ai, N. Yin, and K. Li, “Revisiting multimodal emotion recognition in conversation from the perspective of graph spectrum,” arXiv preprint arXiv:2404.17862, 2024.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Taheseen Shaikh Abdul Aziz, Vinodha Ramalingam, Nandini Prasad K S, David Neels Ponkumar Devadhas and Arun Kumar;
Methodology: Taheseen Shaikh Abdul Aziz and Vinodha Ramalingam;
Writing- Original Draft Preparation: Taheseen Shaikh Abdul Aziz, Vinodha Ramalingam, Nandini Prasad K S, David Neels Ponkumar Devadhas and Arun Kumar;
Investigation: Taheseen Shaikh Abdul Aziz and Vinodha Ramalingam;
Supervision: Nandini Prasad K S, David Neels Ponkumar Devadhas and Arun Kumar;
Writing- Reviewing and Editing: Taheseen Shaikh Abdul Aziz, Vinodha Ramalingam, Nandini Prasad K S, David Neels Ponkumar Devadhas and Arun Kumar;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Authors thank Reviewers for taking the time and effort necessary to review the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Taheseen Shaikh Abdul Aziz
Department of Marketing and Business Analytics, Faculty of College of Business and Tourism, University of Prince Mugrin, Madina, Saudi Arabia.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Taheseen Shaikh Abdul Aziz, Vinodha Ramalingam, Nandini Prasad K S, David Neels Ponkumar Devadhas and Arun Kumar, “Integrating Emotion Aware AI for Hyper Personalized Consumer Targeting in Next Generation Man Machine Computing Environments”, Journal of Machine and Computing, vol.5, no.4, pp. 2019-2037, October 2025, doi: 10.53759/7669/jmc202505158.
