AUTOMATED YOUTUBE VIDEO SUCCESS ANALYZER USING AI

The proposed system presents an effective approach for predicting the popularity of YouTube videos before publication using a combination of Machine Learning and Deep Learning techniques. By analyzing key factors such as thumbnails, captions, and video content, the system provides a comprehensive evaluation of a video’s engagement potential. The integration of Convolutional Neural Networks (CNN) for image analysis and Natural Language Processing (NLP) for text analysis ensures that both visual and contextual features are effectively captured. This multimodal approach improves prediction accuracy compared to traditional single- feature systems.   The use of additional tools such as OpenCV, Pillow, Librosa, Whisper, and MoviePy enhances the system’s ability to process diverse data types, including images, text, audio, and video. Training the model over multiple epochs (15–25 epochs) allows it to learn patterns efficiently and improve performance iteratively. The system not only predicts the likelihood of a video becoming viral but also provides actionable suggestions, enabling content creators to optimize their videos before uploading. This makes the system practical and valuable in real-world applications.   Despite its advantages, the system has certain limitations. The prediction accuracy depends heavily on the quality and size of the dataset used for training. Additionally, external factors such as audience behavior, timing, and trending topics can influence video performance, which may not always be captured by the model. The use of simplified models may also limit generalization across different content categories or niches.   Future work can focus on enhancing the system by integrating real-time trend analysis using live data from YouTube and other platforms. Advanced deep learning techniques, such as multimodal transformers, can be incorporated to improve feature integration and prediction accuracy. Expanding the dataset to include additional engagement metrics like comments, shares, and watch time can further strengthen the model. Moreover, improving the recommendation engine to provide more personalized and adaptive suggestions can make the system more effective and user-friendly.

[1]     D. V. Lindberg and H. K. H. Lee, “Optimization under constraints by applying an asymmetric entropy measure,” J. Comput. Graph. Statist., vol. 24, no. 2, pp. 379–393,   Jun.        2015,     doi: 10.1080/10618600.2014.901225. [2]     B. Rieder, Engines of Order: A Mechanology of Algorithmic Techniques. Amsterdam, Netherlands: Amsterdam Univ. Press, 2020. [3]     I. Boglaev, “A numerical method for solving nonlinear integro-differential equations of Fredholm type,” J. Comput. Math., vol. 34, no. 3, pp. 262–284, May 2016, doi: 10.4208/jcm.1512-m2015-0241. [4]     K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” International Conference on Learning Representations (ICLR), pp. 1–14, 2015. [5]     K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016. [6]     A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017. [7]     H. T. Cheng, L. Koc, J. Harmsen, et al., “Wide & Deep Learning for Recommender Systems,” Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10, 2017. [8]     A. Vaswani, N. Shazeer, N. Parmar, et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems (NeurIPS), pp. 5998–6008, 2017. [9]     J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of NAACL- HLT, pp. 4171–4186, 2019. [10]   M. Tan and Q. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” Proceedings of the International Conference on Machine Learning (ICML), pp. 6105–6114, 2019.