Abstract
The author of a manuscript leaves behind footprints which can be traced back to identify the person. This paper attempts to solve such a text categorization issue by distinguishing the actual author of a document from within a pool of claimants using various Machine Learning tools. The techniques have been shown to achieve success in resolving controversies as and when they arise in the domain of Authorship Attribution. The corpus comprises assorted documents of three separate short-story writers. The process involves utilizing unsupervised K-Means Clustering technique initially to extract some select stylometric features or attributes from the text corpus. Thereafter several Supervised Learning techniques have been implemented to classify the author’s identity correctly. Amongst these the ANN classifier emerges as the best technique with an accuracy of 93.33%.
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References
Mitchell, T.M.: Machine Learning. The McGraw-Hill Companies, Inc. (1997)
Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60(3), 538–556 (2009)
Juola, P.: Authorship attribution: foundations and trends. Inf. Retrieval 1(3), 233–334 (2008)
Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Automatic text categorization in terms of genre and author. Comput. Linguist. 26(4), 471–495 (2000)
Luyckx, K., Daelemans, W.: Shallow text analysis and machine learning for authorship attribution. LOT Occas. Ser. 4(45), 149–160 (2005)
Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing-style features and classification techniques. J. Am. Soc. Inform. Sci. Technol. 57(3), 378–393 (2006)
Kourtis, I., Stamatatos, E.: Author identification using semi-supervised learning. In: Pro-ceedings of the 2011 Conference on Multilingual and Multimodal Information Access Evaluation (Lab and Workshop Notebook Papers), Amsterdam, The Netherlands (2011)
Ouamour, S., Sayoud, H.: Authorship attribution of ancient texts written by ten arabic travelers using a SMO-SVM classifier. In: International Conference on Communications and Information Technology (ICCIT), pp. 44–47. IEEE, Hammamet, Tunisia (2012)
Elayidom, M.S., Jose, C., Puthussery, A., Sasi, N.K.: Text classification for authorship attribution analysis. Adv. Comput. An Int. J. (ACIJ) 4(5), 1–10 (2013)
Schwartz, R., Tsur, O., Rappoport, A., Koppel, M.: Authorship attribution of micro-messages. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Lan-guage Processing, pp. 1880–1891. ACM, Seattle, Washington, USA (2013)
Stuart, L.M., Tazhibayeva, S., Wagoner, A.R., Taylor, J.M.: On identifying authors with style. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 3048–3053. IEEE, Manchester, UK (2013)
Castillo, E., Cervantes, O., Vilarino, D., Pinto, D.: Author attribution using a graph based representation. In: International Conference on Electronics, Communications and Computers (CONIELECOMP), pp. 135–142. IEEE, Cholula, Mexico (2015)
Ahmed, A., Mohamed, R., Mostafa, B.: Authorship attribution in Arabic poetry using NB, SVM, SMO. In: 11th International Conference on Intelligent Systems: Theories and Applications (SITA), pp. 1–5. IEEE, Mohammedia, Morocco (2016)
Gómez-Adorno, H., Sidorov, G., Pinto, D., Vilariño, D., Gelbukh, A.: Automatic author-ship detection using textual patterns extracted from integrated syntactic graphs. Sensors 16(9), 1374 (2016)
Banga, R., Mehndiratta, P.: Authorship attribution for textual data on Online Social Networks. In: Proceedings of 2017 Tenth International Conference on Contemporary Computing (IC3), pp. 1–7. IEEE, Noida, India (2017)
Reddy, P.B., Reddy, T.R., Chand, M.G., Venkannababu, A.: A New Approach for Author-ship Attribution. Information and Decision Sciences, pp. 1–9. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-7082-3_4
Rexha, A., Kröll, M., Ziak, H., Kern, R.: Authorship identification of documents with high content similarity. Scientometrics 115(1), 223–237 (2018). https://doi.org/10.1007/s11192-018-2661-6
Srinivasan, L., Nalini, C.: An improved framework for authorship identification in online messages. Clust. Comput. 22(5), 12101–12110 (2019)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers (2012)
Ullah, F., Wang, J., Jabbar, S., Al-Turjman, F., Alazab, M.: Source code authorship attribution using hybrid approach of program dependence graph and deep learning model. IEEE Access 7, 141987–141999 (2019)
Abuhamad, M., Rhim, J., AbuHmed, T., Ullah, S., Kang, S., Nyang, D.: Code authorship identification using convolutional neural networks. Futur. Gener. Comput. Syst. 95, 104–115 (2019)
Alrabaee, S., Karbab, E.B., Wang, L., Debbabi, M.: BinEye: towards efficient binary authorship characterization using deep learning. In: European Symposium on Research in Computer Security, pp. 47–67. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29962-0_3
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Gupta, S., Patra, T.K., Chaudhuri, C. (2022). Role of Machine Learning in Authorship Attribution with Select Stylometric Features. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_86
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