Text Classification Using Machine Learning and Deep Learning Models

Abstract

In all applications where data plays an important role such as universities, business, research institutions, technology intensive companies and government funding agencies, maintaining irregular data is a big challenge. For an entity (object, place, thing), most of the data is in irregular form. Till now data analytics or text mining examine the entity relationships in a dataset to obtain significant patterns that reflects the information that is there in the dataset. This information is used in making decisions. “Text analytics translates text into numbers and numbers” in order makes up the data and assists to detect patterns. If the data is more organized, the better is the analysis, and eventually the decisions would be better. Manual processing of every bit of data is difficult and also to classify the data. This causes intelligent text processing tools to emerge in the area of NLP, to analyze linguistic and lexical patterns. Before mining it is significant to revise and be familiar with the nature of data. With the expanding measure of information and requirement for precision or accuracy automation process is required for the text classification. Another attractive research opportunity is constructing complex “text data models using Deep learning systems” which have the capability to carry out intricate NLP tasks with semantic requirements. Data analytics forms the basis of text classification and it can act as the engine behind information exploration. These results could be used for emergent applications that support decision making processes. These decisions assist human beings to improve resources and give the majority of benefits. Future research includes improved methods for parameter optimization that reflects effective knowledge discovery.

Keywords: Statistical Methods, Machine Learning, Text Classification