TKES:A Novel System for Extracting Trendy Keywords from Online News Sites

作     者：Tham Vo Phuc Do 

作者机构：Lac Hong UniversityDong Nai71000Vietnam Thu Dau Mot UniversityBinh Duong72000Vietnam University of Information TechnologyVNU-HCMHo Chi Minh7000Vietnam 

出 版 物：《Journal of the Operations Research Society of China》 (中国运筹学会会刊（英文）)

年 卷 期：2022年第10卷第4期

页      面：801-816页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The work of Tham Vo is supported by Lac Hong University,and funded by Thu Dau Mot University(No.DT.20-031) The work of Phuc Do is funded by Vietnam National University,Ho Chi Minh City(No.DS2020-26-01) 

主　　题：Event detection Burst detection Keyword extraction Kleinberg Burst ranking TKES Text stream 

摘      要：As the Smart city trend especially artificial intelligence,data science,and the internet of things has attracted lots of attention,many researchers have created various smart applications for improving people’s life *** it is very essential to automatically collect and exploit information in the era of industry 4.0,a variety of models have been proposed for storage problem solving and efficient data *** this paper,we present our proposed system,Trendy Keyword Extraction System(TKES),which is designed for extracting trendy keywords from text *** system also supports storing,analyzing,and visualizing documents coming from text *** system first automatically collects daily articles,then it ranks the importance of keywords by calculating keywords’frequency of existence in order to find trendy keywords by using the Burst Detection Algorithm which is proposed in this paper based on the idea of *** method is used for detecting bursts.A burst is defined as a period of time when a keyword is continuously and unusually popular over the text stream and the identification of bursts is known as burst detection *** results from user requests could be displayed ***,we create a method in order to find a trendy keyword set which is defined as a set of keywords that belong to the same *** work also describes the datasets used for our experiments,processing speed tests of our two proposed algorithms.