Author Identification using Sequential Minimal Optimization with rule-based Decision Tree on Indian Literature in Marathi

Abstract Authorship Identification is the task of identifying who wrote a given piece of text from a given set of candidate authors (suspects). The increasingly large volumes of texts on the Internet enhance the great yet urgent necessity for authorship identification. For this purpose, a large amount of work has already been done for the English language. Comparatively, less research has been carried out for Indian regional languages such as Tamil, Telugu, Bengali and Punjabi whereas no such experiment is available for Marathi. In this study presented a strategy for authorship identification of the documents written in Marathi language. Moreover, we adopted a set of fine-grained lexical and stylistic features for the analysis of the text and used them to develop two different models (statistical similarity model and SMORDT-Sequential minimal optimization with rule- based Decision Tree approach). Then, we validated the feature extraction method to show consistent significance in every model used in this experiment. The performance of the proposed approach has been evaluated based on the values of Recall, Precision, F-measure and Accuracy.