On authorship authentication of Arabic articles

An extensive study of the Bag-of-Words approach for gender identification of Arabic articles

“…As for using "big words," both classes use, on average, words of the same character-length. This is actually predictable since in Arabic, words tend to be smaller than English and the difference in word length distribution is of smaller significance when it comes to writing styles [1], [2].…”

“…It is one of the fundamental problems in many fields such as text mining, machine learning, natural language processing, information retrieval, etc., with a vast range of applications such as spam filtering [8], sentiment analysis [9], [10], [11], [5], determining author's characteristics such as identity [12], [3], [2], gender [13], [14], dialect [15], native language [16], political orientation [17], [18], etc. In following paragraphs, we briefly discuss some of recent works on general Arabic TC.…”

“…Arabic has many characteristics that have significant effect on the general authorship analysis problems such as its complexity, its "triglossic" nature, 1 being a derivational and inflectional language, etc. [2], [7].…”

“…In this sense, the AA problems can be thought as variations of the well-known Text Classification (TC) problem where different classes represent different authors (for the authorship attribution problem) or different characteristics of the authors (for the authorship profiling problem) instead of the typically assumed topic (or domain) classes [2]. Each type of the AA problems has its own applications.…”

Classifying and Segmenting Classical and Modern Standard Arabic using Minimum Cross-Entropy

“…[2]. It also can be applied in a large variety of applications such as spam filtering [3], author identification [4]- [6], gender identification [7], [8], sentiment analysis [8]- [11], dialects identification [12], [13], and so on.…”