A Method for Bengali Author Detection Using State of the Arts Supervised Machine Learning Classifiers

Abstract

Text classification is an important topic of study in the area of natural language Processing. To identify the authorship of the provided Bangla text, we create a model using the State of Arts Supervised method. Because our work is a multi-class categorization, we may use it to determine who wrote articles, news, emails, or messages. It can also use to find ghostwriters, identify anonymous authors, and detect plagiarism. This article focuses on the categorization of five Bengali authors. They are well-known writers in Bengali literature and poetry. Humayun Ahmed, Rabindranath Tagore, Muhammad Zafar Iqbal, Kazi Nazrul Islam, and Sarat Chandra Chattopadhyay are the five writers. Data were manually collected from various sources in the novels or books of these five writers, and we contained over 4500 paragraphs. A completely new dataset is created for the experimental evaluation. We preprocess Bengali text for training reasons. Logistic regression, naive Bayes, decision trees, support vector machines, random forests, XG-Boost, and K-nearest neighbor are among the seven classification methods employed. In our experiment, the Support Vector Machine produces the best experimental classification report. Support vector machine gives 82% model accuracy.