Volume 8 • Issue 2 • PP: 25–29 • 2024
Visual Harmony Tailoring Video Recommendations through Text
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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This research develops a novel approach for mood-based YouTube video suggestions. Using cutting-edge textual data analysis techniques, through the application of Natural Language Processing (NLP) techniques combined with sentiment analysis based on the FrameNet framework, users' everyday experiences and feelings are carefully analyzed to determine their current mood in the text. The process of content curation is made easier by the extraction of pertinent video metadata with the help of the YouTube API key. The integration of video metadata with textual mood extraction allows for the development of an extremely engaging and personalized content recommendation system. Users are provided with content that resonates with their current emotional state by matching the recommended movies' mood with the one deduced from the textual input. This improves user satisfaction and enriches their experience.
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References
[1] Niyazov, A., Mikhailova, E., & Egorova, O. (2021, May). Content-based music recommendation system. In 2021 29th Conference of Open Innovations Association (FRUCT) (pp. 274-279). IEEE.
[2] Sajib, M. S. R., Malik, M. A. I., & Islam, M. A. (2018). Video recommendation system for YouTube considering users feedback. Global Journal of Computer Science and Technology, 18(G1), 11-15.
[3] Brbic, M., Rozic, E., & Zarko, I. P. (2012, May). Recommendation of YouTube videos. In 2012 Proceedings of the 35th International Convention MIPRO (pp. 1775-1779). IEEE.
[4] G.PonKumar,ArvindRavindran,HarshadSultanT,S.N. Karthikrishna,T.Lokeshwar,S. Arvindswamy,M. Maheshkumar,B. Dharani. "Power Backup for Failsafe Power System." Journal of Cognitive Human-Computer Interaction, Vol. 3, No. 2, 2022 ,PP. 26-35.
[5] Diaz, Y., Alm, C. O., Nwogu, I., & Bailey, R. (2018, March). Towards an affective video recommendation system. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 137-142). IEEE.
[6] Jain, S., Pawar, T., Shah, H., Morye, O., & Patil, B. (2019, April). Video recommendation system based on human interest. In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) (pp. 1-4). IEEE.
[7] Li, Y., Wang, H., Liu, H., & Chen, B. (2017, September). A study on content-based video recommendation. In 2017 IEEE International Conference on Image Processing (ICIP) (pp. 4581-4585). IEEE.
[8] Lee, J., & Abu-El-Haija, S. (2017). Large-scale content-only video recommendation. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 987-995).
[9] Sweeta Bansal,Karan Kohli,K. K. Vishwakarma,Kush Gupta. "Graph Algo Visualizer." Journal of Cognitive Human-Computer Interaction, Vol. 3, No. 2, 2022 ,PP. 36-41.
[10] Mohamed, A., Sherif, A., Osama, F., Roshdy, Y., Hassan, M. A., & El Ashmawi, W. H. (2019, December). A new challenge on video recommendation by content. In 2019 14th International Conference on Computer Engineering and Systems (ICCES) (pp. 336-341). IEEE.
[11] Dai, Z., Sheng, G., Honggang, Z., Guang, C., Yongsheng, Z., Jifeng, T., & Jun, G. (2014, September). A real-time video recommendation system for live programs. In 2014 4th IEEE international conference on network infrastructure and digital content (pp. 498-502). IEEE.
[12] Tavakoli, M., Hakimov, S., Ewerth, R., & Kismihok, G. (2020, July). A recommender system for open educational videos based on skill requirements. In 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT) (pp. 1-5). IEEE.
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