Custom Dataset Text Classification: An Ensemble Approach with Machine Learning and Deep Learning Models

In academic institutions, commercial enterprises, research centers, technology-heavy businesses, and government funding agencies, maintaining consistent data is a major difficulty. For an entity, which might be anything from an object to a place or thing, most data are irregular. These days, to identify significant patterns that represent the data, entity links in a dataset are investigated by text mining or data analytics. With this knowledge, alternatives are then taken. Analytics creates data and finds patterns by turning words into numbers. In the end, better data organization results in better conclusions. However, classifying and processing each piece of data by hand is difficult. As a result, in the domain of Natural Language Processing (NLP), which looks at grammatical and lexical patterns, intelligent text processing systems have emerged. Before mining, it's imperative to examine and comprehend the nature of the data. Text categorization requires automation because of the increasing volume of data and the requirement for accuracy or precision. It is an interesting study opportunity to develop automatic classifying texts with deep learning methods to handle difficult NLP tasks with semantic constraints. Text categorization is founded on data analytics, which can facilitate information discovery. The majority of the advantages can be obtained by applying these insights to emerging applications that support decision-making, improve resources. Improved techniques for parameter optimization demonstrating effective knowledge discovery will be the focus of future research studies.