@inproceedings{fbc88a53423d4397be4068f99b7a1842,
title = "Text Classification of Digital Forensic Data",
abstract = "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.",
keywords = "Digital forensic, sentiment analysis, text classification",
author = "Christian Nwankwo and Hayden Wimmer and Lei Chen and Jongyeop Kim",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020 ; Conference date: 04-11-2020 Through 07-11-2020",
year = "2020",
month = nov,
day = "4",
doi = "10.1109/IEMCON51383.2020.9284913",
language = "English",
isbn = "9781728184166",
series = "11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "661--667",
editor = "Rajashree Paul",
booktitle = "11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020",
address = "United States",
}