A machine learning approach for stylometric analysis of Bangla literature

The term Stylogenetics refers to the eloquent analysis of authors literary corpora which are based on clustering. While writing, a writer focuses on some frequent things subconsciously. We¹ focused on these things and tried to detect the affinity and divergence of the writing of different authors. In this approach, our proposal is regarding on some particular features to distinguish authors individuality who writes and establishes their own viewpoint on similar issues. Here we assembled Bengali Blogs scripted by twenty Bangladeshi authors of two different fields e.g. Political, Educational and analyzed the corpus. Via our methodology, we evaluated some features such as negative Word frequency in particular position, Rapidity of use of highest length word and sentence, Suffix Count, Use of particular Punctuation, Common Recognizable word frequency, Classification of Parts of speech, Numeric words frequency and so on. First, we trained the system using these features and then distinguished from random data sets using two machine learning approaches, Support Vector Machines (SVM) and Naive Bayes classifier. This proposal provides more accuracy than previously established works as all the collected corpus here, are of different writers writing, on the analogous field.