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Style based Authorship Attribution on English Editorial Documents

by N. V. Ganapathi Raju, Ch. Sadhvi, P. Tejaswini, Y. Mounica
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159 - Number 4
Year of Publication: 2017
Authors: N. V. Ganapathi Raju, Ch. Sadhvi, P. Tejaswini, Y. Mounica
10.5120/ijca2017912899

N. V. Ganapathi Raju, Ch. Sadhvi, P. Tejaswini, Y. Mounica . Style based Authorship Attribution on English Editorial Documents. International Journal of Computer Applications. 159, 4 ( Feb 2017), 5-8. DOI=10.5120/ijca2017912899

@article{ 10.5120/ijca2017912899,
author = { N. V. Ganapathi Raju, Ch. Sadhvi, P. Tejaswini, Y. Mounica },
title = { Style based Authorship Attribution on English Editorial Documents },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 159 },
number = { 4 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume159/number4/26987-2017912899/ },
doi = { 10.5120/ijca2017912899 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:04:49.537344+05:30
%A N. V. Ganapathi Raju
%A Ch. Sadhvi
%A P. Tejaswini
%A Y. Mounica
%T Style based Authorship Attribution on English Editorial Documents
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 4
%P 5-8
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of the authorship attribution is identification of the author/s of unknown document(s). Every author has a unique style of writing pattern. The present paper identifies the unique style of an author(s) using lexical stylometric features. The lexical feature vectors of various authors are used in the supervised machine learning algorithms for predicting the unknown document. The highest average accuracy achieved is 97.22 using SVM algorithm.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Style based Classification Lexical features Function words/Stop words Authorship Attribution/Profiling

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