Support Vector Machine based Pattern Taxonomy Classification Method for Web Usage Mining

Abstract

One of the most active research topics is machine-learning-based text classification, which has several uses, such as topic modelling, rating summarisation, reviews, hate speech identification, spam detection, and sentiment analysis. There are differences in the datasets, training techniques, performance assessment techniques, and comparison methods employed in commonly utilised machine-learning-based research. In this study, we conducted a survey of 224 studies that used machine learning for text classification that were published between 2003 and 2022. However, for complex typesetting articles, rule-based solutions come with a large coding cost. However, the mere use of machine learning techniques necessitates the expensive annotation of complex content types inside the document. Moreover, relying exclusively on machine learning may result in instances when patterns that are readily identified by rule-based techniques are inadvertently retrieved.To scan text, photos, and HTML documents and return results to the search engine, web content mining technologies were required. It directs the search engine to deliver more fruitful outcomes for each query according to its significance. The paper the proposed method WBSVM is analysed different web content mining tools for the extraction of relevant information from the corresponding web page.