SmartSEO: An Intelligent Machine Learning Powered System for Automated Search Engine Optimization
DOI:
https://doi.org/10.68104/Keywords:
Search Engine Optimization, Machine Learning, Natural Language Processing, BERT, Keyword Extraction, Intent ClassificationAbstract
The rapid expansion of digital content has intensified competition for visibility on search engine results pages, making effective Search Engine Optimization (SEO) essential for businesses and content creators. Traditional SEO practices remain largely manual, time-intensive, and inaccessible to small enterprises due to high costs and technical complexity. This paper presents SmartSEO, an intelligent, machine learning powered system designed to automate and streamline the SEO optimization process. The system accepts raw text or URLs as input and processes them through a multi-stage pipeline combining Natural Language Processing, semantic analysis, and predictive analytics. Keyword extraction is implemented using TF-IDF and BERT embeddings, ensuring the identification of high-impact, contextually relevant keywords. Readability analysis is performed using linguistic heuristics including sentence length distribution, lexical density, and syntactic complexity. A dedicated ranking prediction module, trained on historical search engine ranking data, uses regression models to estimate potential content positions after optimization. Additionally, the system integrates a user intent classification mechanism employing supervised learning algorithms, including Naive Bayes and C4.5 Decision Tree, to distinguish between informational and transactional queries. Experimental evaluation across diverse industry datasets demonstrated an average 35% improvement in keyword relevance, a 28% increase in predicted search ranking efficiency, and a 40% reduction in manual optimization time compared to traditional methods.
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