User identification based on short text using recurrent deep learning

Huda Hallawi, Huda Ragheb Kadhim, Zahraa Najm Abdullah, Noor D. AL-Shakarchy, Dhamyaa A. Nasrawi


Technological development is a revolutionary process by this time, it is
mainly depending on electronic applications in our daily routines like
(business management, banking, financial transfers, health, and other essential
traits of life). Identification or approving identity is one of the complicated
issues within online electronic applications. Person’s writing style can be
employed as an identifying biological characteristic in order to recognize the
identity. This paper presents a new way for identifying a person in a social
media group using comments and based on the Deep Neural Network. The
text samples are short text comments collected from Telegram group in Arabic
language (Iraqi dialect). The proposed model is able to extract the person's
writing style features in group comments based on pre-saved dataset. The
analysis of this information and features forms the identification decision.
This model exhibits a range of prolific and favorable results, the accuracy that
comes with the proposed system reach to 92.88% (+/- 0.16%).


Embedding layer; Identification; Long sort term memory; Tokenization

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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