Authorship Verification based on Linguistic Features

Abstract

Abstract This thesis attempts to solve the problem of authorship verification. Authorship verification is a subdomain of authorship analysis and its origins lie in stylometry analysis. However most of the research in authorship analysis is based on authorship identification where authorship verification is rather unexplored. With the increase of digital documents and authors it is very difficult to employ authorship identification solutions. Hence in such cases authorship verification solutions are in necessity. This research focuses on utilizing digital documents with 1000 words, written in English to solve the problem of authorship verification: coming into conclusion about the authorship of a text in dispute by analyzing texts written by some candidate author. To solve this problem three machine learning models were designed employing two feature sets, comprising of linguistic features which are suggested to characterize the writing style of a person, one comprising of stylometric features and other consisting of word frequency based features. One-class support vector machine and two-class support vector machine are used as machine learning models to tackle this problem. Results suggest one-class support vector machine with selected stylometric features does not tackle the problem very well while two-class classification model with stylometric features trained for known author class and unknown author class shows potential in solving the problem if the unknown author class can be properly represented. One-class support vector machine with word frequency based features, shows promising results in solving the authorship verification problem.