Comparison analysis of Bangla news articles classification using support vector machine and logistic

TELKOMNIKATelecommunication,Computing,ElectronicsandControl

Vol.21,No.3,June2023,pp.584∼591

ISSN:1693-6930,DOI:10.12928/TELKOMNIKA.v21i3.23416

❒ 584

ComparisonanalysisofBanglanewsarticlesclassification usingsupportvectormachineandlogisticregression

MdGulzarHussain1,2,BabeSultana1,MahmudaRahman1,MdRashidulHasan1

1DepartmentofComputerScienceandEngineering,FacultyofScienceandEngineering,GreenUniversityofBangladesh, Dhaka,Bangladesh

2DepartmentofComputerApplicationTechnology,SchoolofComputerScienceandArtificialIntelligence,Changzhou University,Changzhou,Jiangsu,China

ArticleInfo

Articlehistory:

ReceivedFeb21,2022

RevisedOct29,2022

AcceptedNov12,2022

Keywords:

Banglanews

Banglatextclassification

Logisticregression

Naturallanguageprocessing

Newsclassification

Supportvectormachine

ABSTRACT

Intheinformationage,Banglanewsarticlesontheinternetarefast-growing.For organizing,everynewssitehasaparticularstructureandcategorization.Newsarticleclassificationisamethodtodetermineadocument’sclassificationbasedonvariouspredefinedcategories.ThisresearchdiscussestheclassificationofBanglanews articlesontheonlineplatformandtriestomakeconstructivecomparisonusingseveralclassificationalgorithms.ForBanglanewsarticlesclassification,termfrequencyinversedocumentfrequency(TF-IDF)weightingandcountvectorizerhavebeenused asafeatureextractionprocess,andtwocommonclassifiersnamedsupportvectormachine(SVM)andlogisticregression(LR)employedforclassifyingthedocuments.It isclearthattheaccuracyoftheexperimentalresultsbyapplyingSVMis84.0%and LRis81.0%fortwelvecategoriesofnewsarticles.Inthisresearchwork,whenwe havemadecomparisontworenownedclassificationalgorithmsappliedontheBangla newsarticles,LRwasoutperformedbySVM.

Thisisanopenaccessarticleunderthe CCBY-SA license.

CorrespondingAuthor:

MdGulzarHussain

DepartmentofComputerScienceandEngineering,FacultyofScienceandEngineering GreenUniversityofBangladesh,Dhaka,Bangladesh

Email:gulzar.ace@gmail.com

1. INTRODUCTION

Textdataisthemostcomprehensivesourceofinformationbutduetoitsunstructurednature,itis difficultandtimeconsumingtodrawinsightsfromit.Advancementinmachinelearning(ML)andnatural languageprocessing(NLP)aremakingiteasiertoanalyzetextdatathroughtextclassification.Textclassificationtechniquesareusedtoorganize,structureandcategorizetextdataforanalysisofsentiment,topic labelling,spamdetection,intentdetectionandsoon[1].Thisstudyappliestextclassificationtechniquesto Banglanewsarticlesasthenewsarticlesarethemostcommonformoftextonline.Manystudieshavebeen conductedontextclassification,anddifferentclassificationtechniquessuchasrules-basedmethod,decision trees,k-nearestneighbors(KNN),naiveBayes,logisticregression,supportvectormachines(SVM),neural networks(NN)andsoonhavebeendeveloped[2].Studiesintheliteratureofvariousresearchpapershave shownthatresearchershavefocusedonclassifyingtextsindifferentlanguages,suchasEnglishandArabic andsoon[3],[4]however,theamountofanalysisontheBengalilanguageismuchless.In[5]haveusedmachinelearningalgorithmstocategorizeBanglanewspaperarticlesintofivedistinctgroups.Theyusedlogistic regression,SVM,andmulti-layerneuralnetworktodoso.Multi-layerdenseneuralnetworkapproachhasa

Journalhomepage: http://telkomnika.uad.ac.id

accuracyof95.50%whichishighercomparedtoothermodels.Thereisarepresentation[6]ofthebehavior ofleast-squaresSVMs,twinSVMs,andleast-squarestwinSVM(LS-TWSVM)classifiersonthenewsdata isshowntohandlemulti-categorydata.AndtheirperformanceevaluationshowedthatLS-TWSVMisthe bestofallthreewith92.96%accuracy.Maisha etal. [7]performsentimentanalysisonBanglanewsusing ”pipeline”classalongwithsixstate-of-the-artsupervisedMLalgorithmswhichincludesdecisiontree(DT), multinomialnaiveBayes(MNB),k-nearestneighbor(KNN),logisticregression(LR),randomforest(RF)and lagrangiansupportvectormachine(LSVM).Randomforestalgorithmoutstandsallotheralgorithmssecuring 98%accuracyinpercentagesplitmethod.Thismethodologycategorizesthecontenteitheraspositiveornegative.RabbinnovandKobilov[8]usedSVM,decisiontreeclassifier,randomforest,LR,andMNBamongsix othermachine-learningalgorithmstoconductmulti-classtextcategorizationofinternetUzbeknewsarticles. ForSVM,theyusedradialbasisfunction(RBFSVM)whichgavethebestaccuracy(86.88%)performance ofotherclassifiers.Severalsupervisedmachinelearningaswellasdeeplearningalgorithmsforcategorizing Bengalinewsdocumentsarediscussedin[9].AnevermethodforclassifyingBanglatextualcontenthasbeen developedby[10].Thedeeplearningrecurrentneuralnetworks(RNN)-basedattentionlayerandtheRNN withBiLSTMachievedaccuracyratesof97.72%and86.56%,respectively.In[11]proposedamodelstructure namedtheDCLSTM-MLPmodelforthecategorizationofnewstextdocuments,anideaofacustomizedalgorithmthatcombinesdeeplearningalgorithmssuchaslongtermshortmemory(LSTM),convolutionalneural network(CNN),andmulti-layerperceptron(MLP).Byapplyingthismodelstructuretheyhavetriedtosolve theproblemsoftextuallength,thecomplexityofextractingfeaturesfromnewscontentandcategorizingnews texteffectivelyandachieved94.82%accuracy.However,themainissueoftheirpaperisthatthenumberof samplesissmall,andthedistributionofdifferenttypesofnewsisunequal,resultinginthemodel’snarrow effectiveness.

Kowsher etal.of[12]usedseveralwordembeddingmethodstoincorporatethetextfromBangla newspaperdata,aswellasmachinelearningalgorithmstocategorizetheincorporatedtext.Adeeprecurrent neuralNetworkwasusedtoprovideanewmethodofevaluatingBanglanewsarticlesin[13].Thedeep recurrentneuralnetworkfeaturingBi-LSTMobtained98.33%inBengalitextcategorization,whichisgreater thanpreviouswell-knownclassificationtechniques.Forclassifyingtextordocuments,manyofsupervised techniquesareusedsuchasKNN,naiveBayes(NB),DT,n-grams,neuralnetworks(NNet).Butaccordingto previousresearchliteraturereviewsSVM[14]ismostfrequentlyusedclassifieralgorithm.In[15]proposed anewsarticleclassificationmodelframeworkbasedondeephybridlearningandcomparedittotraditional textclassificationtodemonstratethesuperiorityofnetworknewstextclassification,anditoutperformsthe standardizedmethodforclassifyingnewstextsintermsofoverallperformance.In[16]showedacomparative analysisamongDT,KNN,NB,andRocchio’salgorithmwheretheirstudiessaidthatSVMoutperformsbetter thanallotherclassifiers.AsidefromtheEnglishdocument,therehasalsobeenextensiveresearchonother languages.In[17]focusedontheclassificationofself-createdIndonesiannewscorpus,whichincludesfour separatecategoriesand472Indonesiannewspaperarticlesfromavarietyofsources.Theyproducedfivemodels withtenepocheachusing377datafortrainingand95testingdata,withCNNhavingthehighestaccuracyrate atabout90.74percentcategorizingIndonesiannewsdata.In[18]conductedonanIndonesiannewscorpus foundthatthecombinationofTF-IDFandMNBbeatsotherclassificationmodelssuchasmultivariateBernoulli naiveBayes(BNB)andSVMwithanaccuracyof85%.IntermsofArabictextclassification,in[19]Arabic medicaltextdocumentsareclassifiedandauthorsusedanrule-basedclassifierforclassificationofArabictext andhavinganaccuracyof90.6%.Theyusedthreeclassificationalgorithm:majorityvoting,ordereddecision list,andK-NNwhichareusedtovalidatethemodel.

AmongallthecoupleworkscoveredintheBangla,Chy etal. [20]workedonBanglalanguagenews classificationandtheyusednaiveBayesclassifier.ButtheproblemisthatinthenaiveBayesmodelifthe testingdatasetcontainsacategoricalvariableofacategorythatwasnotpartofthetrainingdataset,itwill giveitzeroprobabilityandbeunabletomakeanypredictions.Nahar etal. [21]demonstratedacomparative analysisutilizingnaiveBayesclassifier,SVM,andneuralnetworkstofilterBanglasportsandpoliticalnews onlyononlinenetworksfromtextdata.MandalandSen[22]showedthecomparisonperformanceevaluationof the4supervisedlearningtechniques:decisiontree,naiveBayes,k-nearestneighbor,andSVMfortheBengali textcategorization.TheyusedTF-IDFtocompriseafeaturevectorusing1000webdocumentswhichcontains 22,218words.Closetoourresearchwork,byusingTF-IDFasafeatureselectionapproachandSVMasa classifier,theyattainedaclassificationaccuracyof92.57percentfor12categoriesofBanglatextfiles.They used3191textsamplespercategorywherewehavetriedtouse11770articlesoftop12categories(showed

ComparisonanalysisofBanglanewsarticlesclassificationusing...(MdGulzarHussain)

inTable1)from20differentcategories.AndbestofourknowledgetheyusedonlySVMwherewehaveused SVMandlogisticregression(LR)andLRisprimarilyusedtoclassifyobservationsintoadiscretenumber ofcategorieswithrapidclassificationofunknownrecords.Inthisresearchpaper,wehavetriedtoshowa comparativeperformanceanalysisusingSVMandLRwhichcanbringrighteouspathforfutureresearchers whoarewillingtoworkwithBanglalanguageinthisera.

Inthisstudy,LRandSVMareusedtoclassifytheBanglanewsarticlesasthestudy[23]showsthat thelogisticregressionoutperformsothertechniquessuchasrandomforestandknearestneighboursalgorithm. ItclassifiestheBanglanewsarticlesandlabelthemtoacertaintopicbasedontheircontents.Itextractsfeatures fromthecorpususingtheTF-IDFfeaturevectorsinceitisenabledtotheextractionofrelevantfeaturesaswell asremovingcommonwords.

2. METHOD

Theaimofnewsclassificationseemstoallocatecategoriesasperthecontentofanewsarticle.PreprocessingofBanglanewsarticlesandfeaturesetextractionarealsoneededpriortotrainingandmodelconstructionfordocumentclassification,likeEnglishtextclassification.TheoverallBanglanewsarticleclassificationprocesshavebeenusedinthisexperimentasillustratedinFigure1.

Despitethelimitedamountofthedata,Hossain etal. [24]discoveredthattext-graphconvolutional neural(GCN)accomplishedbetterthanGRU-LSTM,BiLSTM,Char-CNN,LSTM,andbidirectionalencoder representationsfromtransformers(BERT)inclassifyingonlineBanglanews.Assofar,thereisascarcityof standarddatasetintheBanglalanguage,soadatasethavebeenpreparedbyscrapingthenewsarticlesfrom variouselectronicnewssitessuchas’https://www.prothomalo.com/’.Atthetimeofscrapping,thearticleswere labeledwiththeircategories.Toassesstherecommendationresults,wehavecollectedaround12.5Klabeled newsarticlesconsistingof20categories.Amongthemtop12categoriesareconsideredforthisresearchwork whichhasmentionedinTable1.

Figure1.Systemarchitectureoftheproposedwork

2.1. Datacollection

2.2. Datapreprocessing

Preprocessingisperformedtominimizethenoiseinthetext,whichhelpstoincreasetheclassifier’s efficacy.Thepreprocessingstepsclearthetextdataandpreparingitfortheartificiallearningmodel.Thenthe tokenizationisperformedontextdocumentsandbreakdownthesequenceofcharactersorwordstogetthe features.Sincetextdocumentscontainsentences,likeasequenceofcharacterorwords,togetthefeatures,it shouldbebrokenintotokens.Tokenizationistheprocesstobreakdownthetextintosentencesandthenwords. Tokenizationistheprocessbywhichthetextisdividedintophrasesandthenwords.Aftertokenization, differentsymbolssuchas!,x,¿,¡,$,%,andnumbers,whicharenotveryimportantforclassification,are excluded.Stop-wordsinBanglaarealsoeliminatedatthesameperiod.In[25]isalistofstop-wordsusedin thisresearch.

2.3. Featureextraction

Thegeneralmethodofconversionofacollectionoftextdocumentstonumericalfeaturevectors isvectorization.Countvectorizertransformsatextdocumentarrayintoatokencountingmatrix:contains tokencountsoccurrencesineachdocument.Asparserepresentationofthenumbersisgeneratedbythis implementation.Ontheotherhand,thepurposeofusingTF-IDFinsteadoftherawfrequenciesofatoken’s occurrenceinagivendocumentistoscaledowntheinfluenceoftokensthatoccurinagivencorpusvery regularly.Andthusempiricallylessinformativecharacteristicsthatoccurinasmallfractionofthetraining corpusareremoved.Itisamethodofdataretrievalthatweightsthefrequencyofwords(TF)andtheinversed documentfrequency(IDF).Everywordhasit’sownTFandIDFratings,respectively.TheTF-IDFweightis theproductofaterm’sTFandIDFscores.Therarerthewordandviceversa,thehighertheTF-IDFscore.

2.4. Classifiers

Inthisstudy,twocommonclassifiers:theSVMclassifierwithalinearkernelandthelogisticregressionhavebeenused.Intextclassification,SVMhasbeenusedeffectively.Andlogisticregressionisa statisticalmethodofdataanalysisinwhichoneormorevariablesareusedtodeterminetheresult.

2.4.1. SVM

Inessence,SVMisasupervisedmachinelearningapproachknownasabinaryclassifier.In[25] useshyper-planetoclassifydataintotwotypes.Supportvectorsareclosertothe-hyper-planedatapointsthat influencethepositionandorientationofthehyper-plane.Thesupportvectorisusedtooptimizetheperimeter oftheclassifier.Byeliminatingthesupportvectors,thehyper-plane’spositionwillchange.SVMrecognizes thefollowingsignfunctionofequation[26]mathematically,

��(x)=sin(wx + b) (1)

where w isan n weightedvectorin Rn .Bydividingthespace Rn intotwohalf-spaceswiththemaximum margin,SVMfindsthehyper-planein(2).

y = wx + b (2)

Generally,SVMisabinaryclassifier,butthestrategieslikeone-to-oneandone-vs-restcanbeusedto expandintoamulti-classclassifier.InSVM,linearandradialbasisfunctionkernelsareusedtomakedecisions. Sincemosttextsarelinearlyseparable,thelinearkernelispreferredfortextclassification.

2.4.2. Logisticregression

Logisticregressionusesalogisticfunctiontoestimateprobabilitiesfortherelationshipbetweenone ormoreindependentvariablesandthedependentvariableofthecategories.Thelogisticfunctionequationis alsoknownasthelogisticcurvewhichisacommon“S”shapedcurvedescribedbythe(3).Thesigmoidcurve isanothernameforthelogisticcurve.

(3)

Where, L isthecurve’shighestvalue, e thebaseofanaturallogarithm(oreuler’snumber), k isthelogistic growthrateorcurvesteepness, v0 isthesigmoidmidpoint’s x-value.

ComparisonanalysisofBanglanewsarticlesclassificationusing...(MdGulzarHussain)

3. RESULTSANDDISCUSSION

TheperformanceofSVMclassifiersandlogisticregrassiononourdata-setisdiscussedinthissection. Alittleanalysisonthedata-setisprovidedhere.Later,theperformanceanalysisofSVMandLRontheBangla textdataandcomparisonwithsomesimilarworksaredemonstratedinTable4.

3.1. Datasetanalysis

Among12.5thousandofarticleswith20categoriesweused11770articlesoftop12categoriesin thisresearchwork.Bar-chartfornumberofnewsarticlesofeachcategoriesaregiveninFigure2.InFigure 3generatedwordcloudbasedonTF-IDFandcountvectorizerfromtotalarticlesaregiven.Thisisgenerated beforerunningthepreprocessingonthedata.

Wecanseemanyunnecessarywordsareavailableinthecloudsandthecloud-basedonTF-IDF isdifferentthanthecloud-basedoncountvectorizervalue.Butsomewordsarehavingaroundthesame importanceforbothTF-IDFandcountvectorizervalue.Wehavesplitthedataintotwoparts,fortrainingthe model,80%ofthedataisusedandtheremaining20%isusedtotesttheperformance.

TELKOMNIKATelecommunComputElControl,Vol.21,No.3,June2023:584–591

Figure2.NumbersoftheBanglanewsarticlesforeachcategory

Figure3.WordcloudforthearticlesusedbasedonTF-IDF(left)andcountvectorizer(right)

3.2. Performancemeasures

In-textcategorization,avarietyofevaluationmetricsareemployed.Theprecision,recall,andFmeasureareamongthemostwidelyusedperformancemeasuresconsideredinourexperiments.Eachcategory’sprecision,recall,andF-measureexperimentaremeasuredforSVMandLR.TheF-measureisaveraged toassessperformancethroughoutcategories.Microaverageandmacroaveragearethetwotypesofaverage valueandthemacroaverageisused.Table2showhowtwoclassifiersperformedonourdata-setcorpusin termsofprecision,recall,andF-measure.OntheotherhandTable3showscomparisonofaccuracy,average precision,averagerecallandaverageF1-scoreresultforSVMandLRclassifier.

Table2.SVMandLRclassifierresultsof12categories

ItisobservedthatSVMwithlinearkernelachievesaccuracyof0.84andLRachieves0.81from Table3andSVMoutperformsLRincaseofaverageprecision,recallandF1-score.ThoughfromTable2it isfoundthatforsomeclassesorcategoriesLRoutperformsSVMincaseofindividualprecision,recalland F1-score.Figure4showstheconfusionmatrixforSVMclassifierwiththelinearkernelandlogisticregression respectively.Table4showsthecomparisonbetweentherecentworksusingSVMclassifierfindingswiththe resultsofthisresearchfinding.Thecomparisonshowsthatourresearchworkutilizingthecombinationof TF-IDFandcountvectorizerfeaturesintheSVMclassifierachievesbetteraccuracythanotherrecentworks whichalsousedSVMintheirresearchworks.

Table3.Accuracy,averageprecision,averagerecallandaverageF1-scoreresultofSVMandLRclassifier AccuracyPrecision(Avg)Recall(Avg)F1-score(Avg)

Figure4.Confusion-matrixforSVM(left)andLR(right) ComparisonanalysisofBanglanewsarticlesclassificationusing...(MdGulzarHussain)

ISSN:1693-6930

Table4.ComparisonbetweenthisexperimentandrecentsimilarresearchesusingSVM ExperimentsFeatureusedAccuracy

ThisexperimentTF-IDF+Countvectorizer0.84 Islam etal. [27]TrigramTF-IDF0.80 Rahman etal. [28]TF-IDF0.82 Yeasmin etal. [5]TF-IDF0.83

4. CONCLUSION

Inthefieldofinformationsystems,textcategorizationisacontentiousissue.Inthispaper,wehave usedSVMandLRclassifiersonowndevelopedcorpusandtheirperformanceismeasured.Theproposed methodologyinthisstudyadvocatestheassumptionthattheBanglalanguagecanindeedberightlyclassified usingSVMandLRwithlimitedresources.Theoutcomeofthesuggestedapproachispromising.Still,more precisioncouldhaveattained.Goodoutcomescouldalsohaveobtainedifwehadbeenabletodealwithall ofthenewscategoriesandutilizemultiplecategorizationmethodstocomewithaconstructiveopinion.Inthe next,we’dliketoaddmorecategoriesandcomparetheresultsusingdifferentclassificationalgorithms.

ACKNOWLEDGEMENT

ThisworkwassupportedinpartbytheCenterforResearch,Innovation,andTransformationofGreen UniversityofBangladesh.

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BIOGRAPHIESOFAUTHORS

MdGulzarHussain wasborninNimnagar,DinajpurCity.HereceivedtheBachelorofSciencedegree(2018)inComputerScienceandEngineeringfromtheGreenUniversityof Bangladesh,Dhaka,Bangladesh.Since2019,hehasbeenaLecturerwiththeComputerScience andEngineeringDepartment,GreenUniversityofBangladesh,Dhaka,Bangladesh.Heiscurrently studyingMScinComputerScienceandTechnologyinChangzhouUniversity,Changzhou,Jiangsu, China.HeisalsoaffiliatedwithIEEEasstudentmemberandIAERasprofessionalmember.HisresearchinterestsincludeArtificialIntelligence,MachineLearning,DeepLearning,NaturalLanguage Processing,TextMining,andTopicModeling.Hecanbecontactedatemail:gulzar.ace@gmail.com.

BabeSultana wasbornin,Cox’sBazar,Bangladesh,in1994.ShereceivedherB.Sc.DegreeinComputerScienceandEngineeringfromGreenUniversityofBangladesh(GUB)in2018.At presentsheisworkingasaLecturer,Dept.ofCSE,GreenUniversityofBangladesh.Also,herpublicationwasabout”Multi-modeProjectSchedulingwithLimitedResourceandBudgetConstraints” publishedinInternationalConferenceonInnovationinEngineeringandTechnology(ICIET)27-28 December,2018andshegotBestPaperAwardandIEEEBestPaperAwardonthisconference. HerresearchinterestsincludeTheoryofOptimization,NaturallanguageProcessing,Renewableand SustainableEnergy.Shecanbecontactedatemail:babecse@gmail.com.

MahmudaRahman recivedherMaster’sinCSfromJahangirnagarUniversityandB.Sc. inCSEfromGreenUniversityofBangladesh.ShehasrecievdViceChancellor’sGoldMedalaward forheroutstandingacademicperformancein4thconvocationofGreenUniversityofBangladesh. ShehasbeenworkingasaLecturer,Dept.ofCSE,GreenUniversityofBangladeshsince2019. HerresearchinterestincludesNaturalLanuageProcessing,MedicalimageprocessingandMachine learning.Shecanbecontactedatemail:aurthi018@gmail.com.

MdRashidulHasan receivedtheBachelorofSciencedegreeinComputerScienceand EngineeringfromGreenUniversityofBangladesh.HewasborninPabna,Bangladeshin1994.His researchinterestincludeMachineLearning,NaturalLanguageProcessing,andDataMining.Hecan becontactedatemail:rashidulhasan777@gmail.com.

ComparisonanalysisofBanglanewsarticlesclassificationusing...(MdGulzarHussain)