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2018 | OriginalPaper | Chapter

Investigation of Text Attribution Methods Based on Frequency Author Profile

Authors : Polina Diurdeva, Elena Mikhailova

Published in: Databases and Information Systems

Publisher: Springer International Publishing

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Abstract

The task of text analysis with the objective to determine text’s author is a challenge the solutions of which have engaged researchers since the last century. With the development of social networks and platforms for publishing of web-posts or articles on the Internet, the task of identifying authorship becomes even more acute. Specialists in the areas of journalism and law are particularly interested in finding a more accurate approach in order to resolve disputes related to the texts of dubious authorship. In this article authors carry out an applicability comparison of eight modern Machine Learning algorithms like Support Vector Machine, Naive Bayes, Logistic Regression, K-nearest Neighbors, Decision Tree, Random Forest, Multilayer Perceptron, Gradient Boosting Classifier for classification of Russian web-post collection. The best results were achieved with Logistic Regression, Multilayer Perceptron and Support Vector Machine with linear kernel using combination of Part-of-Speech and Word N-grams as features.

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Title: Investigation of Text Attribution Methods Based on Frequency Author Profile
Authors: Polina Diurdeva
Elena Mikhailova
Publisher: Springer International Publishing
Book: Databases and Information Systems
Print ISBN: 978-3-319-97570-2

Electronic ISBN: 978-3-319-97571-9

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-97571-9_25

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