TY - JOUR
T1 - Text Classification of Digital Forensic Data
AU - Nwankwo, Chrisitan Sunday
AU - Wimmer, Hayden
AU - Chen, Lei
AU - Kim, Jongyeop
PY - 2020/12/22
Y1 - 2020/12/22
N2 - This research aims to propose a model to classify text messages that extracted from the smart phone using forensic software and several machine learning algorithms. The data analysis procedure subdivided into physical extraction, relevant partitions, logical extraction, digital forensic analysis, and text classification. In the text classification step, the final result derived by applying sentiment analysis and k-means clustering algorithm under the control of python application. Through this model, we were able to classify most of the messages correctly as either being positive or negative.
AB - This research aims to propose a model to classify text messages that extracted from the smart phone using forensic software and several machine learning algorithms. The data analysis procedure subdivided into physical extraction, relevant partitions, logical extraction, digital forensic analysis, and text classification. In the text classification step, the final result derived by applying sentiment analysis and k-means clustering algorithm under the control of python application. Through this model, we were able to classify most of the messages correctly as either being positive or negative.
KW - Data mining
KW - Digital forensics
KW - Machine learning algorithms
KW - Smart phones
KW - Software
KW - Software algorithms
KW - Text categorization
UR - https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/144
UR - https://doi.org/10.1109/IEMCON51383.2020.9284913
U2 - 10.1109/IEMCON51383.2020.9284913
DO - 10.1109/IEMCON51383.2020.9284913
M3 - Article
JO - 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) Proceedings
JF - 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) Proceedings
ER -