Drift DetectionMethod Using DistanceMeasures and Windowing Schemes for Sentiment Classification

作     者：Idris Rabiu Naomie Salim Maged Nasser Aminu Da’u Taiseer Abdalla Elfadil Eisa Mhassen Elnour Elneel Dalam 

作者机构：School of ComputingUniverti Teknologi MalaysiaJohor81310Malaysia UTM Big Data CentreIbnu Sina Institute for Scientific and Industrial ResearchUniversiti Teknologi MalaysiaJohor81310Malaysia Ibrahim Badamasi Babangida UniversityLapaiPMB 11Niger SteateNigeria UNITAR Graduate SchoolUNITAR International UniversityTierra CrestJln SS6//3Petaling Jaya47301SelangorMalaysia Department of Information Systems-Girls SectionKing Khalid UniversityMahayil62529Saudi Arabia Department of Mathematics-Girls SectionKing Khalid UniversityMahayil62529Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第74卷第3期

页      面：6001-6017页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups(Project under Grant Number(RGP.2/49/43)) 

主　　题：Data streams sentiment analysis concept drift ensemble classification adaptive window 

摘      要：Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online *** to changes in data distribution,commonly referred to as concept drift,mining this data stream is a challenging problem for *** majority of the existing drift detection techniques are based on classification errors,which have higher probabilities of false-positive or missed *** improve classification accuracy,there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data *** paper presents an adaptive unsupervised learning technique,an ensemble classifier based on drift detection for opinion mining and sentiment *** improve classification performance,this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data *** a drift is detected,the proposed method builds and adds a new classifier to the *** add a new classifier,the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the *** this end,a weighting mechanism is used to calculate the weight of each classifier,which decides the contribution of each classifier in the final classification *** experiments were conducted on real-world datasets and the resultswere evaluated on the false positive rate,miss detection rate,and accuracy *** proposed method is also compared with the state-of-the-art methods,which include DDM,EDDM,and PageHinkley with support vector machine(SVM)and Naive Bayes classifiers that are frequently used in concept drift detection *** all cases,the results show the efficiency of our proposed method.