From Impact Factor to Deep Learning: A Comprehensive Review of Journal Ranking with NLP and AI Techniques

Academic journal ranking systems evaluate research quality, guide authors in selecting publication platforms, and support institutional evaluations. This study investigates how DL and NLP techniques can enhance academic journal rankings by providing a more comprehensive assessment of scholarly influence than traditional metrics (citation counts, Impact Factors, network-based algorithms). Content-based citation analysis and hybrid models integrating network metrics with semantic feature extraction offer an exhaustive view of academic impact. DL-based recommendation systems assist researchers in selecting suitable journals, with studies indicating their superiority over traditional methods. However, ethics, data quality, model interpretability, and adaptability still remain. Academic publication, research assessment, and scholarly communication are affected, and the report urges more incredible research and collaboration to overcome problems and utilize these technologies' potential.