Authorship Identification using Generalized Features and Analysis of Computational Method
Authorship Identification is being used for forensics analysis and humanities to identify the author of anonymous text used for communication. Authorship Identification can be achieved by selecting the textual features or writing style. Textual features are the important elements for Authorship Identification .It is therefore important to analyze them and identify the most promising features. This paper tries to identify and analyze promising generalized features and computational methods for authorship Identification. The performed experiments in the authorship identification task shows that, the support vector machine classifier used as computational method can achieve better results with identified generalized feature set.
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