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Messaging Activity Reconstruction with Sentiment Polarity Identification

  • Panagiotis AndriotisEmail author
  • George Oikonomou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9190)

Abstract

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.

Keywords

Smartphone Forensics Text-mining Short-text messages 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of BristolBristolUK

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