Clustering Crimes Related Twitter Posts using WordNet and Agglomerative Algorithm

dc.contributor.author	Sandagiri, S.P.C.W
dc.contributor.author	Kumara, B.T.G.S
dc.contributor.author	Banujan, K.
dc.date.accessioned	2020-12-31T15:49:32Z
dc.date.available	2020-12-31T15:49:32Z
dc.date.issued	2020
dc.identifier.uri	http://ir.kdu.ac.lk/handle/345/2853
dc.description.abstract	Abstract: Crime is a major problem faced today by society. Crimes have affected the quality of life and economic growth badly. We can identify the crime patterns and predict the crimes by detecting and analyzing the historical data. We can use social media like twitter to detect crimes related activities. Because Twitter users sometimes convey messages related to his or her surrounding environment via twitter. In this paper, we proposed a machine learning approach to cluster the crime-related twitter post based on the crime category. The empirical study of our prototyping system has proved the effectiveness of our proposed clustering approach. Keywords: Clustering, WordNet, Agglomerative algorithm, SVM	en_US
dc.language.iso	en	en_US
dc.subject	Clustering	en_US
dc.subject	WordNet	en_US
dc.subject	Agglomerative algorithm	en_US
dc.subject	SVM	en_US
dc.title	Clustering Crimes Related Twitter Posts using WordNet and Agglomerative Algorithm	en_US
dc.type	Article Full Text	en_US
dc.identifier.journal	13th International Research Conference General Sir John Kotelawala Defence University	en_US
dc.identifier.pgnos	24-30	en_US