Deep Sentimental Analysis of the Arabic Medieval Documents.

This paper discusses a new approach motivated by the contemporary procedures of short texts' classification by means of deep learning methodology. The method considers the tested collection and the collections of imposters as sequences of short patterns like "tweets". A deep neural network combined with a CNN layer accomplished by the LSTM ones is trained to distinguish between the imposters' collections at the first stage. The obtained network classifies the bathes of the test collection, transforming its texts into signals, making it possible to label them according to their individual writing style. The suggested procedure intends for new attitudes to study textual material applied to recognize authorship attributes of medieval Arabic documents from a novel computational perspective. Evaluating the outcomes proves the method's high reliability demonstrated in the paper on investigating texts questioningly attributed to Al-Ghazali, comparing them with The Holy Quran and the "One Thousand and One Night". The proposed methodology suggests a new look at the perusal of medieval documents' inner structures and possible authorship from the short-patterning and signals processing perspectives. [ABSTRACT FROM AUTHOR]