Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman, 19328, Jordan and Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
The research examines the application of Natural Language Processing (NLP) and Deep Convolutional Neural Networks (Deep-CNN) in forecasting social media activity. The study aims to improve Social Media Awareness (SMA) by integrating these technologies. The research utilizes 500,000 Facebook posts to develop a model that predicts user behavior based on the number of posts, post count, and sentiment. The study found that image and text data performed better than unpredictability methods, demonstrating the importance of data fusion in predicting user behavior. This could revolutionize online advertising methods and establish the basis for a Decision-Making System (DMS) that includes advertising data analytics and Artificial Intelligence (AI). The research project used a hybrid model to predict user participation in advertisements, while a random model predicted post count, share count, and post sentiment for 60% of each blog post. The models accurately predicted post sentiment, post count, and share count 61%, 62%, and 65% of the time, setting an acceptable standard for future studies.
Keywords
Sentiment Analysis, Natural Language Processing, Social Media Advertising, Customer Visions, Machine Learning, Brand Monitoring.
Y. Ma, Y. Liu, L. Chen, G. Zhu, B. Chen, and N. Zheng, “BrainCLIP: Brain Representation via CLIP for Generic Natural Visual Stimulus Decoding,” IEEE Transactions on Medical Imaging, pp. 1–1, 2025, doi: 10.1109/tmi.2025.3537287.
B. Liu, Y. Xu, C. Xu, X. Xu, and S. He, “Open-set Mixed Domain Adaptation via Visual-Linguistic Focal Evolving,” IEEE Transactions on Circuits and Systems for Video Technology, pp. 1–1, 2025, doi: 10.1109/tcsvt.2025.3551234.
A. Saha and S. Kumar Maji, “Enhanced RSVQA Insight Through Synergistic Visual-Linguistic Attention Models,” IEEE Geoscience and Remote Sensing Letters, vol. 22, pp. 1–5, 2025, doi: 10.1109/lgrs.2025.3592253.
Z. Wan et al., “Frequency Spectrum Adaptor for Remote Sensing Image–Text Retrieval,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 18922–18940, 2025, doi: 10.1109/jstars.2025.3589786.
P. Fang and F. Wang, “Text-Image Generation Using Visual-Language Transformer Parallel Decoding,” 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), pp. 567–572, Apr. 2024, doi: 10.1109/icsp62122.2024.10743787.
N. A. Shah, V. VS, and V. M. Patel, “LQMFormer: Language-Aware Query Mask Transformer for Referring Image Segmentation,” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12903–12913, Jun. 2024, doi: 10.1109/cvpr52733.2024.01226.
G. Ananthakrishnan, S. Sengan, M. E, T. Palanisamy, V. B, and S. B, “Mitigating Data Tampering in Smart Grids Through Community Blockchain Driven Traceability Frameworks,” Journal of Machine and Computing, pp. 1745–1762, Jul. 2025, doi: 10.53759/7669/jmc202505138.
D. Jaime, J. E. Haddad, and P. Poizat, “Navigating and Exploring Software Dependency Graphs Using Goblin,” 2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR), pp. 369–371, Apr. 2025, doi: 10.1109/msr66628.2025.00029.
V. Veeramachaneni, P. K. Mallick, S. Rout, and P. K. Pareek, “Efficient Task Scheduling for Cloud Environments with MSA: Enhancing Makespan and Resource Utilization,” 2025 International Conference on Emerging Systems and Intelligent Computing (ESIC), pp. 740–747, Feb. 2025, doi: 10.1109/esic64052.2025.10962694.
H. M. Ali, J. J. J, S. G, T. Palanisamy, V. Rachapudi, and S. Sengan, “Operating Cash Flow Ranking Using Data Envelopment Analysis with Network Security Driven Blockchain Model,” Journal of Machine and Computing, pp. 1839–1851, Jul. 2025, doi: 10.53759/7669/jmc202505144.
S. H. Nowfal, S. Sengan, J. S. D. G, S. Bhatta, S. V, and V. B, “The Diagnosis of Heart Attacks: Ensemble Models of Data and Accurate Risk Factor Analysis Based on Machine Learning,” Journal of Machine and Computing, pp. 589–599, Jan. 2025, doi: 10.53759/7669/jmc202505046.
C. Ruoxing, W. Jianning, A. Basem, R. A. Hussein, S. Salahshour, and S. Baghaei, “Examining the application of strategic management and artificial intelligence, with a focus on artificial neural network modeling to enhance human resource optimization with advertising and brand campaigns,” Engineering Applications of Artificial Intelligence, vol. 143, p. 110029, Mar. 2025, doi: 10.1016/j.engappai.2025.110029.
J. J. Atia et al., “Exploring the Impact of Social Media on Attaining HbA1c Targets in Individuals with Type 2 Diabetes Mellitus in Iraq: A Cross-Sectional Study,” Clinical Medicine Insights: Endocrinology and Diabetes, vol. 17, Jan. 2024, doi: 10.1177/11795514241293346.
I. M. Abdulkareem, F. K. AL-Shammri, N. A. A. Khalid, and N. A. Omran, “Proposed Approach for Object Detection and Recognition by Deep Learning Models Using Data Augmentation,” International Journal of Online and Biomedical Engineering (iJOE), vol. 20, no. 05, pp. 31–43, Mar. 2024, doi: 10.3991/ijoe.v20i05.47171.
N. S. Alsharafa, S. Sengan, S. S. T, A. D, S. V, and R. K, “An Edge Assisted Internet of Things Model for Renewable Energy and Cost-Effective Greenhouse Crop Management,” Journal of Machine and Computing, pp. 576–588, Jan. 2025, doi: 10.53759/7669/jmc202505045.
A. M. A. Al-Sabaawi, M. H. Hussein, and M. Dalli, “Classifying Items with the Rating Values 3 Using Text Reviews to Improve the Recommendation Accuracy in the Collaborative Filtering Approach,” Karbala International Journal of Modern Science, vol. 11, no. 1, Jan. 2025, doi: 10.33640/2405-609x.3393.
A. K. Abdul Raheem and B. N. Dhannoon, “A Novel Deep Learning Model for Drug-drug Interactions,” Current Computer-Aided Drug Design, vol. 20, no. 5, pp. 666–672, Oct. 2024, doi: 10.2174/0115734099265663230926064638.
V. Veeramachaneni, P. K. Mallick, S. Rout, and P. K. Pareek, “Two-Dimensional Multi-Deep CNNs for Accurate and Robust Software Defect Detection: A Performance-Driven Approach,” 2025 International Conference on Emerging Systems and Intelligent Computing (ESIC), pp. 734–739, Feb. 2025, doi: 10.1109/esic64052.2025.10962737.
M. Turkulainen, X. Ren, I. Melekhov, O. Seiskari, E. Rahtu, and J. Kannala, “DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing,” 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2421–2431, Feb. 2025, doi: 10.1109/wacv61041.2025.00241.
J. Hu, T. An, Z. Yu, J. Du, and Y. Luo, “Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion,” 2025 5th International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 520–524, Feb. 2025, doi: 10.1109/iccece65250.2025.10985689.
Y. Peng, D. Z. Chen, and M. Sonka, “U-Net V2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation,” 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), pp. 1–5, Apr. 2025, doi: 10.1109/isbi60581.2025.10980742.
Z. Wan et al., “Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation,” 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1734–1744, Feb. 2025, doi: 10.1109/wacv61041.2025.00176.
K. Vellimalaipattinam Thiruvenkatasamy, H. M. A. Ghanimi, S. Sengan, and M. G. Alharbi, “An online tool based on the Internet of Things and intelligent blockchain technology for data privacy and security in rural and agricultural development,” Scientific Reports, vol. 15, no. 1, Jul. 2025, doi: 10.1038/s41598-025-13231-9.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Muntadher Idrees Ali, Savitha K, Aseel Smerat, Kolluru Suresh Babu, Sivakumar G and Gracy Theresa W;
Writing- Original Draft Preparation: Muntadher Idrees Ali, Savitha K, Aseel Smerat, Kolluru Suresh Babu, Sivakumar G and Gracy Theresa W;
Visualization: Muntadher Idrees Ali, Savitha K and Aseel Smerat;
Investigation: Kolluru Suresh Babu, Sivakumar G and Gracy Theresa W;
Supervision: Muntadher Idrees Ali, Savitha K and Aseel Smerat;
Validation: Kolluru Suresh Babu, Sivakumar G and Gracy Theresa W;
Writing- Reviewing and Editing: Muntadher Idrees Ali, Savitha K, Aseel Smerat, Kolluru Suresh Babu, Sivakumar G and Gracy Theresa W; All authors reviewed the results and approved the final version of the manuscript.
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The authors would like to thank to the reviewers for nice comments on the manuscript.
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Corresponding author
Savitha K
Department of Computer Science, School of Commerce, Vel Tech University, Avadi, Chennai, Tamil Nadu, India.
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Cite this article
Muntadher Idrees Ali, Savitha K, Aseel Smerat, Kolluru Suresh Babu, Sivakumar G and Gracy Theresa W, “Multimodal Deep Learning Model for Measuring the Impact of Social Media Advertising Using Visual Linguistic Representation Learning”, Journal of Machine and Computing, vol.5, no.4, pp. 2566-2573, October 2025, doi: 10.53759/7669/jmc202505197.
