Using DL Measure and Distance to Assess Text Mining Document Classification Problems

In this study, we take an asymptotic approach to the issue of document categorization in text mining. Some intriguing aspects may be shown graphically through the formalisation of statistical hypothesis testing as a text mining task. Due to its experimental efficacy in numerous case studies, various heuristics are used to practical analysis in the text mining issue. This method will provide the necessary theoretical explanation for the efficacy of text mining approaches. Documents are sorted using a distance metric in word vector area and deep learning algorithms. The study also examines the effectiveness of distance measures from the fresh perspective of asymptotic analysis.