TY - GEN
T1 - Automated Classification of Criminal and Violent Activities in Thailand from Online News Articles
AU - Thaipisutikul, Tipajin
AU - Tuarob, Suppawong
AU - Pongpaichet, Siripen
AU - Amornvatcharapong, Amornsri
AU - Shih, Timothy K.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/21
Y1 - 2021/1/21
N2 - Criminal and violent activities are a universal concern that affects a society's nature of life and economic dynamics. With dramatically increasing crime rates, law enforcement agencies have begun to show attention in utilizing machine learning approaches to analyze crime patterns to protect their communities. However, there are only a few studies that carried out experiments to classify Thai crime news articles into their proper categories. Also, the comparison of various machine learning algorithms toward this task has still been under-investigated. Therefore, in this paper, we aim to develop a framework to automate the classification and visualization of criminal and violent activities from online Thai news articles. Six classifiers are employed to classify crime news articles into one of the five crime categories including Burglary, Drug, Murder, Accident, and Corruption. The results have shown that Support Vector Machine and Logistic Regression approaches outperform other classifiers in terms of Accuracy, Precision, Recall, and F-Measure metrics.
AB - Criminal and violent activities are a universal concern that affects a society's nature of life and economic dynamics. With dramatically increasing crime rates, law enforcement agencies have begun to show attention in utilizing machine learning approaches to analyze crime patterns to protect their communities. However, there are only a few studies that carried out experiments to classify Thai crime news articles into their proper categories. Also, the comparison of various machine learning algorithms toward this task has still been under-investigated. Therefore, in this paper, we aim to develop a framework to automate the classification and visualization of criminal and violent activities from online Thai news articles. Six classifiers are employed to classify crime news articles into one of the five crime categories including Burglary, Drug, Murder, Accident, and Corruption. The results have shown that Support Vector Machine and Logistic Regression approaches outperform other classifiers in terms of Accuracy, Precision, Recall, and F-Measure metrics.
KW - Crime and Violence Classification
KW - Information Extraction
KW - News Articles
KW - Supervised Machine Learning
KW - Text Mining
UR - http://www.scopus.com/inward/record.url?scp=85105866006&partnerID=8YFLogxK
U2 - 10.1109/KST51265.2021.9415789
DO - 10.1109/KST51265.2021.9415789
M3 - 會議論文篇章
AN - SCOPUS:85105866006
T3 - KST 2021 - 2021 13th International Conference Knowledge and Smart Technology
SP - 170
EP - 175
BT - KST 2021 - 2021 13th International Conference Knowledge and Smart Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference Knowledge and Smart Technology, KST 2021
Y2 - 21 January 2021 through 24 January 2021
ER -