DETECTING TERRORISM-RELATED ARTICLES ON THE E-GOVERNMENT USING TEXT-MINING TECHNIQUES
In this paper, a method based on text-mining techniques for detecting terror-related articles on the e-government is proposed. The proposed method consists of several stages: 1) creation of terror-related vocabulary; 2) creation of a semantic network of words; 3) morphological analysis of words; 4) initial filtration of documents; 5) calculation of the semantic similarity between words by using a semantic network of words; 6) determination of semantic similarity between sentences; 7) determination of semantic similarity between documents; 8) classification of documents. Hybrid similarity measures are introduced to calculate the similarity among words, sentences and documents. A hybrid classification method combining the kNN, Bayes and new proposed Ramiz-Gunay methods for identification of terror-related articles is proposed (pp. 36-46).
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