Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Detecting Suspicious Texts Using Machine Learning Techniques

Version 1 : Received: 31 July 2020 / Approved: 2 August 2020 / Online: 2 August 2020 (14:38:13 CEST)

A peer-reviewed article of this Preprint also exists.

Sharif, O.; Hoque, M.M.; Kayes, A.S.M.; Nowrozy, R.; Sarker, I.H. Detecting Suspicious Texts Using Machine Learning Techniques. Appl. Sci. 2020, 10, 6527. Sharif, O.; Hoque, M.M.; Kayes, A.S.M.; Nowrozy, R.; Sarker, I.H. Detecting Suspicious Texts Using Machine Learning Techniques. Appl. Sci. 2020, 10, 6527.

Journal reference: Appl. Sci. 2020, 10, 6527
DOI: 10.3390/app10186527

Abstract

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rapid growth of online content which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier `tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

Subject Areas

Natural Language Processing; Suspicious Text Detection; Bengali Language Processing; Machine Learning; Text Classification; Feature Extraction; Suspicious Corpora

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