Unify Framework for Crime Data Summarization using RSS Feed Service

Tichakorn NETSUWAN, Kraisak KESORN

Abstract


This research presents online crime news analysis using text mining, Natural Language Processing framework (General Framework for Text Mining: GATE), and data warehouse (DW) technologies. The proposed framework aims at extracting key features of crime data available on newspaper website and classifies them into crime categories which are later transformed into a star schema for speedy retrieving and online analytical processing (OLAP). This system can present data in multidimensional structure to perform data analytics to support police officers for determining the security policies to protect locals and tourists who live in the risk areas. The main novelty of this framework is the demonstration of using information available through RSS feed service to generate reports to support decision making. The experimental results show that the extracted data from the Internet can effectively represent the actual crime data occurred in the study areas (low error rate) and allow data analysts to get an insight of the information represented through OLAP.

Keywords


Crime news, data analytics, OLAP, text mining, data warehouse

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References


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