Search Engine Optimization by using Machine Learning for Web Page Classification

Abstract

This study presents an original way for classifying websites into three groups depending on how well their content adheres to search engine optimization (SEO) standards. It is based on expert knowledge and machine learning algorithms. Here the classifiers developed, then trained to separate a sample of unknown (a web page) into 1 of 3 categories which recognize critical traits that influence the level of page alteration. By hand, subject-matter experts manually label the data in the training set. The experimental findings demonstrate that machine learning may be used to anticipate how much a web page will be adjusted to the SEO recommendations. The proposed technique is valuable because it paves the way for agents in software and system expert that automatically identify web-pages or particular web pages area, that need to be changed to adhere to SEO best practices, perhaps obtain improved search engine ranks as a result. The findings of this study also advance the topic of determining the best positions for ranking variables that search engines use to rank webpages. Earlier research’ results indicated the page title, meta description, and H1 tag (header), and body text should all be considered when constructing a web page are supported by the experiments conducted for this study. The creation of a fresh data set of manually annotated web pages as a result of this study is another output that might be applied to future research.