VideoIQ - A Multimodal Video SEO Intelligence System

This paper presented VideoIQ, a multimodal video SEO intelligence system designed to automate the generation of search-optimized metadata directly from video content. The system integrates speech-based transcription, natural language processing, and content-driven trend analysis within a unified end-to-end pipeline. Experimental evaluation demonstrated strong performance for videos with clear audio across educational and techni-cal domains, with keyword precision exceeding 80% and ROUGE-1 scores above 0.55. These results validate the effec-tiveness of the proposed approach in generating meaningful and actionable SEO insights. The system reduces manual effort in content optimization and provides a scalable solution for improving video discov-erability. However, its performance is influenced by audio quality and reliance on external processing modules. Future work will focus on three key directions: (i) integra-tion of real-time trend data to enhance the trend scoring model, (ii) incorporation of visual content analysis to support non-speech videos, and (iii) extension to multilingual processing for broader applicability. Overall, VideoIQ demonstrates the potential of combining multimodal analysis and AI-driven techniques for intelligent content optimization in modern video platforms.

[1]     M. H. A. Abdullah et al., “Systematic Literature Review of Information Extraction From Textual Data,” IEEE Access, vol. 11, pp. 10562, 2023. [2]     S. A. Alshalif et al., “Alternative Relative Discrimination Criterion Feature Ranking Technique,” IEEE Access, vol. 11, pp. 71755, 2023. [3]     A. Arca et al., “Automated Tag Enrichment by Semantically Related Trends,” 2023. [4]     S. Bostan et al., “Improving Ranking Using Hybrid Custom Embedding Models,” Journal of Web Engineering, 2023. [5]     E. B. Boztepe et al., “Audio-Visual Content Understanding using Mul-timodal Deep Learning,” SAUCIS, vol. 5, no. 2, 2022. [6]     D. Cao et al., “Hashtag Recommendation for Micro-Videos,” Knowledge-Based Systems, vol. 203, 2020. [7]     Q. Cao and H. Huang, “Video Visual Relation Detection,” IEEE TKDE, vol. 35, no. 12, 2023. [8]     X. Chen et al., “Graph Neural Networks for Tag Recommendation,” 2023. [9]     Q. Cheng et al., “Cross-Modal Retrieval Framework,” IEEE TCSVT, vol. 34, no. 7, 2024. [10]    X. Du et al., “Personalized Video Recommendation,” IEEE TKDE, vol. 32, no. 3, 2020.