Using Machine Learning in Web Page Categorization for Search Engine optimization.

Abstract

This research introduces an innovative approach to classifying websites based on their compliance with SEO standards. By merging expert insights with machine learning algorithms, the study develops classifiers capable of accurately sorting web pages into three categories. These classifiers pinpoint key factors that impact the level of page optimization. The training phase entails experts manually labeling data. Experimental findings underscore the efficacy of machine learning in gauging a web page's adherence to SEO guidelines. This method holds significance as it automates the identification of pages needing optimization to enhance search engine rankings. Moreover, the research sheds light on the optimal arrangement of ranking variables utilized by search engines, reinforcing previous research. Additionally, the establishment of a new dataset comprising manually annotated web pages proves invaluable for future research initiatives. KEYWORDS: Machine learning, on-page optimization, classification, SEO optimization, Search engine optimization.