Domain Text Classification Using Machine Learning Models

Natural Language Processing (NLP) is used to classify text domains. In all applications where data is critical, such as News media, educational institutes, business organizations, research organizations, scientific, technology companies, and government organizations maintaining huge every day generated data is a serious challenge. Automated text classification has long been regarded as a critical way for managing and processing a large number of digital documents that are widely distributed and growing. In this paper to classify text domain four algorithms such as SVM, Naive Bayes, Decision Trees, and Random Forest evaluated and proved that SVM have achieved more accuracy than other algorithms.