A Novel Automatic Text Document Classification Using Learning based Text Classification(LbTC) Approach

There are practical uses for text document classification. In actuality, it is crucial to categorize and parse documents with natural language. Utilizing applications like bogus news identification, query tagging, sentiment classification, and spam filtering needs this sort of study. However, because of their ambiguity, open-ended nature, and vastness, text documents provide a difficult Classification challenge. Machine learning (ML) methods became valuable alongside the growth of artificial intelligence (AI) due to their data-driven learning techniques. These methods are seen as highly effective at handling and analyzing vast data sets in depth. Topic segmentation, text categorization, entity identification, machine translation, and text summarization, to name a few, are just a few of the issues that may be resolved with ML approaches. In this study, we introduced an automatic Text Classification system (ATCF), a system that uses shallow and deep neural networks to classify text documents. To implement our system, we proposed an approach called Learning based Text Classification (LbTC). We investigated the performance of several models in comparison. Based on the training provided to ML models, the suggested framework assists in categorizing any type of document. It is compatible with practical applications where the categorization of documents is essential.