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基于ACONSVMHMM混合算法的情感识别研究.docx

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基于ACONSVMHMM混合算法的情感识别研究 Abstract Sentimentanalysishasbeenanimportantresearchtopicinnaturallanguageprocessing,asitcanhelpusdeeplyunderstandtheopinionsandemotionsofpeople.Acommonlyusedapproachtosentimentanalysisistousemachinelearningalgorithmstocategorizetextsintopositive,negative,orneutralcategories.Inthispaper,weproposeanovelACONSVHMhybridalgorithmforsentimentanalysisthatcombinestheadvantagesofSVM,HMM,andACOalgorithms.Theproposedalgorithmhasachievedsuperiorperformanceinsentimentanalysiscomparedtoseveralotherstate-of-the-artalgorithms. Introduction Sentimentanalysishasbecomearapidlygrowingresearchareainthefieldofnaturallanguageprocessingduetoitswiderangeofapplications,suchasmarketingandcustomeropinionanalysis.Sentimentanalysisistheprocessofdeterminingtheemotionaltoneofapieceoftext,suchasanewsarticle,socialmediapost,orproductreview.Thegoalofsentimentanalysisistoclassifythetextaspositive,negative,orneutral,basedonthewriter'stoneandemotionalexpression. Thereareseveralmachinelearningalgorithmsthathavebeenusedforsentimentanalysis,suchasNaïveBayes,LogisticRegression,SupportVectorMachine(SVM),andHiddenMarkovModel(HMM).However,thesealgorithmshavetheirlimitationsintermsofaccuracyandefficiency.Toovercometheselimitations,weproposeanovelACONSVHMhybridalgorithmforsentimentanalysis. ACONSVHMAlgorithm TheACONSVHMalgorithmisahybridoftheSupportVectorMachine(SVM),HiddenMarkovModel(HMM),andAntColonyOptimization(ACO)algorithms.Theproposedalgorithmcombinesthestrengthsofthesedifferentalgorithmstoachievehighaccuracyandefficiencyinsentimentanalysis. TheSVMalgorithmisawidelyusedmachinelearningalgorithmthatiseffectiveinseparatingdataintodifferentcategories.IntheACONSVHMalgorithm,weuseSVMtoclassifytextintopositive,negative,orneutralcategoriesbasedonthefeaturesofthedata. TheHMMalgorithmisastatisticalmodelthatisusedtomodelsequentialdatawithhiddenstates.IntheACONSVHMalgorithm,weuseHMMtomodelthesequentialstructureofthetext,assentimentanalysisisasequencemodelingproblem. TheACOalgorithmisametaheuris