Analytics of machine learning-based algorithms for text classification

• DOI: 10.1016/j.susoc.2022.03.001
• Publication Date: 2022-04-01T18:25:25Z
• Authors: Sayar Ul Hassan; Jameel Ahamed; Khaleel Ahmad
• Keywords: Statistical classification

Text classification is the most vital area in natural language processing which text data automatically sorted into a predefined set of classes. The application wide commercial works like spam filtering, decision making, extracting information from raw data, and many other applications. more significant for enterprises since it eliminates need manual classification, expensive time-consuming mechanism. In this paper, comparative analysis done efficiency different machine learning algorithms on datasets analyzed compared. Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Random Forest (RF) are Learning based used work. Two to make these algorithms. This paper further analyzes techniques employed basis performance metrics viz accuracy, precision, recall f1- score. resullltsss reveals that outperforms models IMDB dataset, kNN SPAM dataset as per results obtained proposed system.