Available Website Names Classification Using Naïve Baye

This paper presents a method for classifying website names using machine learning techniques based on the analysis of URLs from different websites on the internet. The primary objective is to categorize websites as either positive or negative, aiding in access permissions. The proposed method offers advantages such as improved content filtering, increased risk awareness, enhanced access control, and a comparative analysis with Decision Tree and Logistic Regression models. The experimental dataset includes training and testing data of website URLs, along with external datasets for sentiment analysis. The results demonstrate an impressive accuracy rate of 94%, validating the suitability of the method for website name classification. Future work can explore the application of the classification method in network security to detect and block negative websites by classifying them as malicious URLs. This extension would further enhance protection against harmful content and contribute to a more secure online environment.