Genre independent authorship attribution for turkish documents

In this thesis, we propose a scaling algorithm using multivariate analysis for authorship attribution in different document types with heterogeneous properties. The scaling algorithm is inspired by the idea of removing the non-variable background used in capturing moving objects in image recognition systems. This algorithm consists of two steps, which are determining the source-based common features of the documents in different topics and genres and removing these common features from the document vector for uncovering the style of the authors. Authorship attribution differs from other text classification types in terms of text processing techniques. The topic, genre, and target audience affect the author's word choice, causing the author's style to blur. In this context, the author's different types of documents are scaled according to the type which the document belongs to, and the similarity between the documents by the same author or different authors is exposed. In the thesis, classification based accuracy measurements were made by using term and character sequences on different types of documents, such as e-mails, blogs, micro messages, newspaper articles, and novel excerpts. The proposed scaling algorithm achieves the highest accuracy regardless of topic, feature set and genre in any dataset in classification based authorship attribution. In addition, scaling on only the term or character sequence features in the cross-domain and cross-genre datasets is highly competitive with the complex text processing techniques obtained by linguistic analysis.