A Comparative Analysis of Machine Learning Algorithms for Text Analysis [Articol]

Abstract

This article proposes a system of metrics for estimating text fetched from the Internet and selecting the one that should be further summarized. The research examines algorithms and software for determining user preferences, employing natural language processing (NLP) and supervised learning classification methods. An empirical assessment is conducted across academic, security, and non-security domains. The paper concludes with insights on the experimental results and the potential future of the implemented metric system.