MUET logo

Contextual video analytics and recommendations through natural language processing (NLP) and graph machine learning (GML)

Authors

  • Umm-e-Laila

    College of Computer Science & Information Systems, Institute of Business Management, Karachi, Pakistan
    Author

  • Khalid Mahboob

    College of Computer Science & Information Systems, Institute of Business Management, Karachi, Pakistan
    Author

  • Muzammil Ahmed Khan

    Computer Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan
    Author

  • Muhammad Khurram

    University of Technology and Applied Sciences, Nizwa, Sultanate of Oman
    Author

  • Mustafa Ahmed Khan

    College of Computer Science & Information Systems, Institute of Business Management, Karachi, Pakistan
    Author
    https://orcid.org/0009-0008-5656-9163

DOI:

https://doi.org/10.22581/muet1982.0220

Keywords:

Natural Language Processing (NLP) , Sentiment Analysis , Graph ML , Content Analysis, Keyword Extraction

Abstract

This research paper explores an innovative approach to video recommender systems by integrating Natural Language Processing (NLP) techniques, graph databases, and advanced analytics. The smart content recommender system goes beyond traditional title or description-based recommendations, aiming to enhance user experience through content-level searching capabilities and spoken-word analysis within videos. Through sentiment analysis (SA), topic analysis (TA), and keyword extraction (KWE), the system tags and timestamps the video transcripts in a detailed and contextual manner and offers recommendations. Since the study is underpinned by Graph Machine Learning (GML) literature, it is informed by a call for recognizing keywords spoken in videos more efficiently. It responds to questions about recommending content with less explicit information and adds new solutions like voice search and multi-language interface to improve accessibility. This idea may appeal to industries that want to receive videos from the targeted population with state-of-the-art content analysis.

Downloads

References

Claudio Stamile, AldoiMarzullo, Enrico Deusebio. Graph Machine Learning (2021) Book.

Wang, X., & Wang, Y. (2016). Improving Content-based and Hybrid Music Recommendation using Deep Learning. Proceedings of the 22nd ACM International Conference on Multimedia.

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2017). Deep Learning based Recommender System: A Survey and New Perspectives. arXiv preprint arXiv:1707.07435.

Stamile, C., Marzullo, A., & Deusebio, E. (2021). GraphiMachine Learning. Packt Publishing.

Zhang, S., L., Sun, A., & Tay, Y. (2017). Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Computing Surveys, 52(1), 1-38.

Wang, X., He, X., Wang, M., Feng, F., Chua, T. S. (2019). Neural Graph Collaborative Filtering. Proceedings of the 42nd International ACMiSIGIR conferenceion Research and Development in Information Retrieval, 165-174.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pretraining of Deep Bidirectional Transformers for Language Understanding.arXivipreprintiarXiv:1810.04805.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems, 30, 5998-6008.

Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251-1258.

Sun, Y., Zhang, Y., & Zhang, Y. (2020). A Survey on Deep Learning for Recommender Systems. IEEE Access, 8, 186690-186712.

H. S. Nawaz, Z. Shi, Y. Gan, A. Hirpa, J. Dong and H. Zheng, "Temporal Moment Localization via Natural Language by Utilizing Video Question Answers as a Special Variant and Bypassing NLP for Corpora," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 9, pp. 6174-6185, Sept. 2022, doi: 10.1109/TCSVT.2022.3162650.

Zhang, S., L., Sun, A., & Tay, Y. (2017). Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Computing Surveys, 52(1), 1-38.

Wang, X., He, X., Wang, M., Feng, F., Chua, T. S. (2019). Neurali Graph Collaborative Filtering. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 165-174.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.

Vaswani, A., iShazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need—advances in Neural Information Processing Systems, 30, i5998-6008.

Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251-1258.

Sun, Y., Zhang, Y., & Zhang, Y. (2020). A Survey on Deep Learning for Recommender Systems. IEEE Access, 8, 186690-186712.

Medhat, W., Hassan, A. and Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), pp.1093–1113.

Isinkaye, F.O., Folajimi, Y.O. and Ojokoh, B.A. (2015). Recommendation systems: Principles, methods, and evaluation. Egyptian Informatics Journal, 16(3), pp.261–273.

Li, L., Zhang, Z., and Zhang, S. (2021). Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation. Scientific Programming, 2021, pp.1–11.

Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y. and Zhao, L. (2018). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, [online] 78(11), pp.15169–15211

Mei, T., Yang, B., Hua, X.-S. and Li, S. (2011). Contextual Video Recommendation by Multimodal Relevance and User Feedback. ACM Transactions on Information Systems, 29(2), pp.1–24.

J. Liu et al., (2025). "Graph Foundation Models: Concepts, Opportunities, and Challenges," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2025.3548729.

R. H. I. Cajipe, C. A. G. Santos, K. C. G. Español, M. Florence, D. S. Pablo, and A. C. Arguson, (2023). “Video Content Analysis through Computer Vision and Natural Language Processing,” in CS EXPO 2023 V.2.0At: FEU Institute of Technology, 2023, pp. 0–5.

R. Hernández-Pérez, P. Lara-Martínez, B. Obregón-Quintana, L. S. Liebovitch, and L. Guzmán-Vargas, (2024). “Correlations and Fractality in Sentence-Level Sentiment Analysis Based on VADER for Literary Texts,” Information, vol. 15, no. 11, pp. 1–14, 2024, doi: 10.3390/info15110698.

S. Arora, A. Malhotra, A. Kumar and N. Singh, "Sentiment Analysis Unleashed: Leveraging VADER for Evaluation of Public Opinion," (2024). 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2024, pp. 1-6, doi: 10.1109/ICRITO61523.2024.10522172.

Published

2025-07-01

Issue

Vol. 44 No. 3 (2025): July Issue

Section

Articles

License

Copyright (c) 2025 Mehran University Research Journal of Engineering and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Contextual video analytics and recommendations through natural language processing (NLP) and graph machine learning (GML). (2025). Mehran University Research Journal of Engineering and Technology, 44(3), 65-73. https://doi.org/10.22581/muet1982.0220

Similar Articles

41-50 of 83

You may also start an advanced similarity search for this article.