基于中文微博的情感-分析研究

基于中文微博的情感-分析研究

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時間:2019-03-08

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1、華中科技大學碩士學位論文AbstractMicroblogisbecomingamostpopularinternetapplication.Accordingtothestatis-tics,morethan100milliontweetspublichedineveryday.Thesetweetsnotonlyconveythedescriptionoffacts,butalsocontaintheemotionalstatesofmassivemicroblogusers.Andtheseemotionalinformationsmaybehelpfo

2、rusertodecidewhetherbuyaproduct,provideveryimportantreferencevalueforcompaniestomakemarketstrategy,andevenmakemassivedataavailableforgovernmenttomonitoringpublicopinion.Inlightofthis,weproposedasentimentanalysismethodbasedonacombinationofsyntacticdependenciesandtextclassificationtec

3、hniquesforChinesetweets.Themethodadoptsthesyntacticdependenciestoperformsentimentanalysis,atthesametime,com-putesaconfidenceforeverytweet.Choosentweetswhichconfidenceaboveacertainthresholdastrainingsamples,trainatwo-stepsentimentclassifierbyusingthecontentfeaturesandmediafeaturesoft

4、weets.Finally,classifythesentimentorientationoftweetsagain.Inaddiation,wealsoproposedamethodthatservescommonemoticonsasthesen-timentclasslabelsoftweetsandimplementsanincrementallearningmethodtotackletheproblemofreal-timesentimentanalysis.Experimentalresultsshowthattheproposedmethodd

5、ramaticallyimprovesthepre-cisionandtherecallby6%and3%repectivelycomparedtothemethodthatonlybasedonsyntacticdependencies.Andtheperformanceofourtwofeaturesetsarealsobetterthanunigramfeatures,theprecisionandtherecallbothare88%intermofsubjectiveclassifier,andtheyare72.1%and71.5%forsenti

6、mentclassifier.Apartfromthis,themediafeaturesaregoodfortracklingtheproblemofreal-timesentimentanalysis.Keywords:ChineseMicroblog,SentimentAnalysis,Syntacticdependencies,TextClassi-ficationII華中科技大學碩士學位論文目錄摘要·············································································

7、······IABSTRACT···········································································II1緒論1.1課題研究背景···································································(1)1.2課題的研究目的和意義·······················································(2)1.3國內(nèi)外研究現(xiàn)狀····························

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