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Intelligent Analysis of Arabic Tweets for Detection of Suspicious Messages by AlGhamdi, Mohammed A.; Khan, Murtaza Ali is a Engineering article available to read on EtoBox.

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With the widespread use of messaging via social networks such as Twitter, Instagram, and Facebook, it is becoming imperative for researchers to devise intelligent systems for data analytics in the range of domains like business, health, communication, security, etc. The complex morphological and syntactic structure of Arabic sentences makes them difficult to analyze. This paper presents an intelligent system to analyze Arabic tweets for detecting suspicious messages. We acquired Arabic tweet data from micro-blogging social network Twitter via Twitter Streaming Application Programming Interface and save it in a required file format. The system tokenizes and preprocesses the tweet dataset. Manual labeling is performed on tweet dataset for suspicious (label 1) and not-suspicious (label 0) classes. The labeled tweet dataset is used to train a classifier using supervised machine learning algorithms for the detection of suspicious activities. During the testing phase, the system processes unlabeled tweet data and detects either it belongs to a suspicious or not-suspicious class. We tested the system using six supervised machine learning algorithms: (1) decision tree, (2) k-nearest neighb

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Author

AlGhamdi, Mohammed A.; Khan, Murtaza Ali

Publisher

Springer-Verlag; Springer Verlag; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1319-8025)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)