  Authorship identification is a process in which the author of a text is
identified. Most known literary texts can easily be attributed to a certain
author because they are, for example, signed. Yet sometimes we find unfinished
pieces of work or a whole bunch of manuscripts with a wide variety of possible
authors. In order to assess the importance of such a manuscript, it is vital to
know who wrote it. In this work, we aim to develop a machine learning framework
to effectively determine authorship. We formulate the task as a single-label
multi-class text categorization problem and propose a supervised machine
learning framework incorporating stylometric features. This task is highly
interdisciplinary in that it takes advantage of machine learning, information
retrieval, and natural language processing. We present an approach and a model
which learns the differences in writing style between $50$ different authors
and is able to predict the author of a new text with high accuracy. The
accuracy is seen to increase significantly after introducing certain linguistic
stylometric features along with text features.
