数字取证数据的文本分类

Christian Nwankwo, H. Wimmer, Lei Chen, Jongyeop Kim
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引用次数: 1

摘要

本研究旨在提出一个模型,使用取证软件和几种机器学习算法对从智能手机中提取的短信进行分类。数据分析过程分为物理提取、相关分区、逻辑提取、数字取证分析和文本分类。在文本分类步骤中,在python应用程序的控制下,应用情感分析和k-means聚类算法得出最终结果。通过这个模型,我们能够正确地将大多数信息分类为积极的或消极的。
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Text Classification of Digital Forensic Data
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.
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