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Messaging activity reconstruction with sentiment polarity identification

Andriotis, Panagiotis; Oikonomou, George


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Dr Panos Andriotis
Senior Lecturer in Computer Forensics and Security

George Oikonomou


Theo Tryfonas

Ioannis Askoxylakis


© Springer International Publishing Switzerland 2015. Sentiment Analysis aims to extract information related to the emotional state of the person that produced a text document and also describe the sentiment polarity of the short or long message. This kind of information might be useful to a forensic analyst because it provides indications about the psychological state of the person under investigation at a given time. In this paper we use machine-learning algorithms to classify short texts (SMS), which could be found in the internal memory of a smartphone and extract the mood of the person that sent them. The basic goal of our method is to achieve low False Positive Rates. Moreover, we present two visualization schemes with the intention to provide the ability to digital forensic analysts to see graphical representations of the messaging activity of their suspects and therefore focus on specific areas of interest reducing their workload.


Andriotis, P., & Oikonomou, G. (2015). Messaging activity reconstruction with sentiment polarity identification. Lecture Notes in Artificial Intelligence, 9190, 475-486.

Journal Article Type Conference Paper
Acceptance Date Aug 2, 2015
Publication Date Jan 1, 2015
Journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 9190
Pages 475-486
Book Title Human Aspects of Information Security, Privacy, and Trust
Keywords smartphone, forensics, text-mining, short-text messages
Public URL
Publisher URL
Additional Information Title of Conference or Conference Proceedings : Third International Conference, HAS 2015, Held as Part of HCI International 2015