TY - GEN
T1 - Automated crime report analysis and classification for e-government and decision support
AU - Ku, Chih Hao
AU - Leroy, Gondy
PY - 2013
Y1 - 2013
N2 - With an increasing number of anonymous crime tips and reports being filed and digitized, it is generally difficult for crime analysts to process and analyze crime reports efficiently. We are developing a decision support system (DSS), combining Natural Language Processing (NLP) techniques, a document similarity measure, and machine learning, i.e., a Naïve Bayes' classifier, to support crime analysis and classify which crime reports discuss the same and different crime. The DSS is developed with text mining techniques and evaluated with an active crime analyst. We report here on an experiment that includes two datasets with 40 and 60 crime reports and 16 different types of crimes for each dataset. The results show that our system achieved the highest classification accuracy (94.82%), while the crime analyst's classification accuracy (93.74%) is slightly lower.
AB - With an increasing number of anonymous crime tips and reports being filed and digitized, it is generally difficult for crime analysts to process and analyze crime reports efficiently. We are developing a decision support system (DSS), combining Natural Language Processing (NLP) techniques, a document similarity measure, and machine learning, i.e., a Naïve Bayes' classifier, to support crime analysis and classify which crime reports discuss the same and different crime. The DSS is developed with text mining techniques and evaluated with an active crime analyst. We report here on an experiment that includes two datasets with 40 and 60 crime reports and 16 different types of crimes for each dataset. The results show that our system achieved the highest classification accuracy (94.82%), while the crime analyst's classification accuracy (93.74%) is slightly lower.
KW - Classification
KW - Natural Language Processing
KW - Similarity measures
UR - http://www.scopus.com/inward/record.url?scp=84880520960&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880520960&partnerID=8YFLogxK
U2 - 10.1145/2479724.2479732
DO - 10.1145/2479724.2479732
M3 - Conference contribution
AN - SCOPUS:84880520960
SN - 9781450320573
T3 - ACM International Conference Proceeding Series
SP - 18
EP - 27
BT - dg.o 2013 - Proceedings of the 14th Annual International Digital Government Research Conference
T2 - 14th Annual International Digital Government Research Conference: From E-Government to Smart Government, dg.o 2013
Y2 - 17 June 2013 through 20 June 2013
ER -