Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis

DOI: 10.4018/IJSWIS.2021040104

International Journal on Semantic Web and Information SystemsVolume 17 • Issue 2 • April-June 2021

Copyright©2021,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited.

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Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment AnalysisShailendra Kumar Singh, Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, India

https://orcid.org/0000-0001-9658-1441

Manoj Kumar Sachan, Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, India

ABSTRACT

Therapidgrowthofinternetfacilitieshasincreasedthecomments,posts,blogs,feedback,etc.,onalargescaleonsocialnetworkingsites.Thesesocialmediadataareavailableinanunstructuredform,whichincludesimages,text,andvideos.Theprocessingofthesedataisdifficult,butsomesentimentanalysis,informationretrieval,andrecommendersystemsareusedtoprocesstheseunstructureddata.Toextracttheopinionandsentimentofinternetusersfromtheirwrittensocialmediatext,asentimentanalysissystemisrequiredtodevelop,whichcanworkonbothmonolingualandbilingualphonetictext.Therefore,asentimentanalysis(SA)systemisdeveloped,whichperformswellondifferentdomaindatasets.Thesystemperformanceistestedonfourdifferentdatasetsandachievedbetteraccuracyof3%onsocialmediadatasets,1.5%onmoviereviews,1.35%onAmazonproductreviews,and4.56%onlargeAmazonproductreviewsthanthestate-of-arttechniques.Also,thestemmer(StemVerb)forverbsoftheEnglishlanguageisproposed,whichimprovestheSAsystem’sperformance.

KeyWoRdSCode-Mixed Phonetic Text, Opinion Verb, Sentiment Analysis, Sentiment Score, Social Media, Stemmer

1. INTRodUCTIoN

Withtheenhancementofinternetservicesandfacilities,socialnetworkingsitessuchasYouTube,GooglePlus,LinkedIn,Twitter,andFacebookhaveincreasedrapidly(PressTrustofIndia,2013).Thesesocialnetworkingsitesprovidefacilitiestosharetheusers’feelings,emotions,comments,feedbacks,and reviewsover the internet.Thus, the sizeof suchcontentover socialmedia (SM)increasesexponentiallydaybyday.MostoftheSMtextcontentsarewrittenusingmorethanonelanguageandcalledcode-mixedlanguage.ThetextoflanguagesotherthanEnglishiswrittenusingRomanscript’salphabetscalledphonetictext.ThephonetictextmixedwithEnglishlanguagetext,butthereisnofixedformatfortheseSMtexts(Duttaetal.,2015).Thesecontentsareusedasinputtexttoextractinformation,opinion,textsummarization,etc.,usingvariouslinguisticcomputations,naturallanguageprocessing,textmining,andinformationretrievalsystems(S.K.Singh&Sachan,2019b).

Opinionminingorsentimentanalysis(SA)isasub-fieldoftextminingandisoneofthemostrecentresearchtopicsofinterest(Pang&Lee,2008).TheSAisrelatedtopredictingandanalyzinghiddeninformation,emotion,andfeelingsfromthewrittentext.TheSAiswidelyusedtoanalyzefeedbacksongovernmentregulationandpolicyproposed,toanalyzethecustomers’likes/dislikes,to know the product demand, brand reputation, real-world event monitoring and analyzing of


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politicalpartydemand,competitorsproducts’meritordemeritanalyzes,andsubtaskcomponentofrecommendersystem(Bonadimanetal.,2017;D’Andreaetal.,2015).InApril2013,90%ofconsumersdecidedtopurchasethingsorservicesbasedononlinereviews(Pengetal.,2014).

Theuser-writtentextsareclassifiedusingSAintotwoorthreeclassesbasedondifferentformatssuchaspositive/negative/neutral, like/dislike,andgood/bad(Hopkenetal.,2017;S.K.Singh&Sachan,2019b).MostlytwoapproachesareusedtoclassifythetextusingSA,suchas(i)feature-basedand(ii)bag-of-words.Machinelearningtechniquesarebasedonfeatures,whilelexicon-basedtechniquesusebag-of-wordsapproaches.InSAofproductsandservices,machinelearningiswidelyused,butthebag-of-wordsareusedforsocialissues(Karamibekr&Ghorbani,2012).Machinelearningsystemsaretrainedonthelabeleddataset(s)andclassifythetestingdataset(s)basedonthetrainedsystem.Thelexicon-basedtechniquesarecategorizedastwoapproachessuchascorpus-basedanddictionary-based(S.K.Singh&Sachan,2019b).Thetextclassificationusingthedictionary-basedapproachdependsupontheopinionword’ssentimentscore.Thisdictionaryconsistsofopinionwordsandtheirsentimentscores(Alharbi&Alhalabi,2020).Theopinionwordsarenouns,verbs,adverbs,andadjectives,whichactasfeaturesindictionary-basedapproachasdiscussedinthearticles(Hopkenetal.,2017;Shamsudinetal.,2016;P.K.Singhetal.,2015;R.K.Singhetal.,2020;S.K.Singh&Sachan,2019b).TheseopinionwordsareusedtodevelopopiniondictionariessuchasSentiWordNet3.0(Baccianellaetal.,2010),thelatestSenticNet4(Cambriaetal.,2016),etc.

The SM text is available in monolingual, bilingual, and multilingual. The processing ofmultilingualtextsisadifficult taskascomparedtobilingualandmonolingualtext.Monolingualtextcanbeeasilyprocessed,butbilingualtextuptosomeextent.Taboadaetal.(2011)developed“SemanticOrientationCALculator(SO-CAL)”usingadictionary,whichincludesopinionwords(nouns,adverb,verbs,andadjectives)alongwiththeirpolarityandstrengthvalue(Taboadaetal.,2011)andachievedthebestperformanceintermoftheaccuracy70.10%onmoviedatasetamongotherdatasets.In2012,Karamibekr&Ghorbanidevelopedasentimentclassificationsysteminwhichverbsareconsideredthecoreelementofthesystemandcreatedanopinionverbdictionaryof440verbsand1726termsforthetermopiniondictionary.Thevalueofpolarityisassignedtoeachwordinthedictionaryrangingfrom+2to-2,andtheirsystemwastestedonadatasetrelatedtosocialissueswithanaccuracyof65%(Karamibekr&Ghorbani,2012).P.K.Singhetal.,(2015)usednegationhandlingrulesandadictionary-basedapproachtoclassifythesocialissuesrelateddatasetintopositiveornegativeclassandachievedanaccuracyof79.16%fornegativesentences.Iqbaletal.,(2015)proposedaBias-awarethresholding(BAT)methodwiththecombinationofAFINNandSentiStrengthtoreducethebiasinthelexicon-basedmethodforSAandobtained69%ofaccuracyusingNaïveBayesclassifier.

In2016,Bhargavaetal.developedasystemofSAforcode-mixedsentencesof theEnglishlanguagewith four Indian languages (Hindi,Telugu,Bengali, andTamil) using the count-basedapproachandlanguage-specificSentiWordNetstodeterminetheword’spolarityvalue.Theirsystemclassifiedsentencesbasedonthenumberofnegativeandpositivewordspresentwithinthesentenceandobtained54.4%ofFscoreonEnglish_Hindimixeddataset.Inthesameyear,alexical-basedmethodwasproposedby(Shamsudinetal.,2016)toclassifytheFacebookcommentswritteninMalaylanguageusingtheverb,adverb,andnegation.TheVerb+Negationcombinationachievedthehighestaccuracyof52.12%.

In2017,aSAsystemforaspecificdomaintextwasproposedby(Cruzetal.,2017),usingadictionary-basedapproach.Thesystemwastestedondifferentdatasetssuchaskitchen,book,moviereviews,andagriculture;andachievedthebestperformanceonmoviereviews(Fscoreof66%).Amulti-tasktri-trainingmodelbasedondeeplearningwasproposedby(Ruder&Plank,2018).Theyhavetrainedtheirmodelwiththreemodel-specificoutputsjointlyandachieved79.15%accuracyonAmazonproductreviews.Later,in2018Hanetal.proposedamethodtoreducethebiasinthelexicon-basedapproach.TheyusedSentiWordNetandtriedtoimproveaccuracybutachieved69.52%accuracyand69.65%Fscore(Hanetal.,2018).BaoxinWang(2018)proposedadeeplearning-based


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DisconnectedRecurrentNeuralNetworksmodel,andYu&Liu(2018)proposedSlicedRecurrentNeuralNetworksmodel.TheyhavetestedtheirmodelsonlargeAmazonproductreviews,buttheirsystemachievedlessthan65.00%accuracy.

SentiVerbsystemwasdevelopedby(S.K.Singh&Sachan,2019b)andtestedonGSTdatasetandmoviereviews.Thesystemconsideredverbasafeature,andobtained82.50%accuracyontheGSTdatasetand71.30%accuracyonmoviereviewsevenwithsmalldatasetavailability.In2020,aneventnamedSemEval-2020Task9wasconductedonSAofcode-mixedtweets,andthehighestperformancewasreportedas75.00%Fscorebyoneparticipantonbilingual(English_Hindi)textusingXLM-Rmethod(Patwaetal.,2020).In2020,amethodtoautomaticallylabelthedocumentwithpolarityorsentimentclasswasproposedandreducehumanintervention,processtime,andcost(Kansaletal.,2020).Theirpolaritydetectiontask,alongwiththesentimentclassificationsystem,achieved81.98%accuracyonAmazonproductreviewsusingalogisticregressionapproach.

It isobserved thatmostof theresearchworkhasbeenconductedonSA/opinionminingformonolinguallanguagetextinsteadofbilingualandmultilingualtextforresource-scarcelanguages(Loetal.,2017)asperTable1andrecentsurveyarticles(Drus&Khalid,2019;Guellil&Boukhalfa,2015;Hussein,2018;Medhatetal.,2014;Ravi&Ravi,2015;Serrano-Guerreroetal.,2015;R.K.Singhetal.,2020;Yueetal.,2019).ThehighestaccuracyonSMdatasetis82.5%;71.3%onmoviereviews(S.K.Singh&Sachan,2019b);81.98%onAmazonproductreviews(Kansaletal.,2020)and64.43%onlargeAmazonproductreviews.MostofthestudiesotherthanEnglishlanguageareinChinese,Japanese,German,Spanish,French,Italian,Swedish,Arabic,andRomanian(Loetal.,2017).Therefore,asmallernumberofstudiesandexperimentshavebeenconductedforbilingualandmultilingualtextandhavenotshownadequatesystemperformance.Thereisnoworkfoundrelatedtothesentimentclassificationofcode-mixedbilingual(EnglishandPunjabi)phonetictext.Themachinelearningtechniquesrequirealargeamountoflabeleddataset(s)fortrainingpurposes,andcode-mixedphonetictextdataset(s)isnotavailablesufficiently.Hence,thereisaneedtodesignanddevelopaSAsystem,whichcangivebetterperformanceforbothmonolingualandbilingualcode-mixedphonetictext.Withthisperspective,asystemisdesignedanddevelopedforSAofbilingual(EnglishandPunjabi)code-mixedphonetictextusingarule-basedclassifieranddictionary-basedapproach.Thefirsttimetheverbopiniondictionary(VOD)forPunjabiwordsisdevelopedwiththemotivationoftheopinionverbdictionary(OVD)oftheEnglishlanguage(S.K.Singh&Sachan,2019b).

Thesignificantcontributionsare:Firsttimebilingual(English&Punjabi)code-mixedphonetictextsareconsideredforSA.Inthisarticle,asentimentanalysissystemisproposedtoclassifycode-mixed bilingual (English_Punjabi) phonetic text using handcrafted rules, rule-based classifiers,anddictionaries.Thebilingual code-mixed testingdatasets (86,400 reviews) aregenerated frommonolingualtext.Thespellchecker,VOD,negationwords,positivewords,negativeprefixes,negationhandlingrules,stopwords,andStemVerbsystemisdevelopedandusedtoextractthewriter’ssentimentfromtheirwrittentext.TheVODofthePunjabiandEnglishlanguageisdeveloped,whichincludes653wordsofPunjabiand677Englishwords.Therootverblistof3582wordsandanirregularverblistof222wordsisdeveloped.TheStemVerbsystem(Sub-systemofSAsystem)isproposedtoextracttherootverbfromtheinflectedformoftheEnglishlanguageverbandobtain22.83%betteraccuracythantheexistingsnowballstemmersystem.TheproposedSAsystemachievedbetterperformancethanstate-of-artapproachesonallfourbenchmarkdatasets,formonolingualtext(betteraccuracyof3%onSMdataset,1.5%onmoviereviews,1.35%onAmazonproductreviews,and4.56%onlargeAmazonproductreviews).

Thisarticleisorganizedasfollows:Section1describestheintroductionofSAandtheideaaboutpreviousresearchworksonSA.Section2providesdetailedinformationabouttheproposedframeworkofthesystemandanexplanationofthedifferentphasesfortheimplementationofthesystem.Section3isrelatedto theperformanceevaluationof theproposedsystemonthedifferentdataset(s)andresultanalysisalongwiththecomparisonofperformancewithstate-of-the-arttechniques.Finally,abriefdetailedconclusionofthisarticleisprovidedalongwiththefuturescopeinthelastsection.


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2. PRoPoSed SySTeM FRAMeWoRK ANd IMPLeMeNTATIoN

The proposed system classifies the code-mixed phonetic text into positive or negative using adictionary-basedapproach.Thissystemconsideredverbwordsasafeature.Thissystemhasbeen

Table 1. Analysis of existing works related to SA

Authors Method Accuracy (%)

F Score (%)

Limitations

(Taboadaetal.,2011) Lexicon-based 70.10 ●Smalldatasize(5100)●OnlyforEnglishlanguagetext

(Karamibekr&Ghorbani,2012)

Dictionary-based 65.00

●Smalldatasize(1016)●Onlytestedonsocialissuedataset●OnlyforEnglishlanguagetext

(Iqbaletal.,2015) BATwithLexicon-based 69.00 ●SystemdesignedonlyforEnglishlanguage

text

(P.K.Singhetal.,2015)


●VerySmalldatasize(48)●Consideredonlynegativecommentsfortesting.●OnlyforEnglishlanguagetext

(Shamsudinetal.,2016)


●VerySmalldatasize(450Facebookcomments)●LimitedtoMalaylanguagetext●Notsufficientaccuracy

(Bhargavaetal.,2016) Sentiwordnet 54.40

●Smalldataset(637)●NotconsideredPunjabiandEnglishcode-mixedtext●Performancenotsufficient

(Cruzetal.,2017) Dictionary-based 66.00 ●Smalldataset(4183)

●OnlyforEnglishlanguagetext

(Ruder&Plank,2018) Multi-tasktri-training 79.15 ●OnlyforEnglishlanguagetext

(Hanetal.,2018) Dictionary-based 69.52 69.60

●Smalldataset(2000)●OnlyforEnglishlanguagetext●OnlyAmazonproductreviews

(BaoxinWang,2018)

DisconnectedRecurrentNeuralNetworks

64.43

●OnlyforEnglishlanguagetext●Notsufficientaccuracy

(Yu&Liu,2018)

SlicedRecurrentNeuralNetworks

61.65

●OnlyforEnglishlanguagetext●Notsufficientaccuracy

(S.K.Singh&Sachan,2019b)

Dictionary-based 82.50 87.04 ●Smalldataset(400)



Dictionary-based 71.30 71.39 ●Smalldataset(2000)


(Patwaetal.,2020) XLM-R 75.00 ●ConsideredonlyHindiandEnglishcode-mixedtext

(Kansaletal.,2020) Logisticregression 81.98 ●Smalldataset(8000)



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designed which can handle bilingual phonetic text and constituted by (a) lower case conversion(b)tokenization(c)spellchecker(d)part-of-speech(POS)tagging(e)stopwordselimination(f)stemming(g)sentimentcalculation(h)documentclassificationasshowninFigure1.

2.1. Testing datasets GenerationTheexistingfourdatasetsintheEnglishlanguageareusedtogeneratebilingual(English_Punjabi)code-mixedphonetic text datasets (shown inTable2)with thehelpof abilingualdictionaryofEnglishandPunjabiwords.ThePunjabiwordsarewritteninphoneticusingRomanscriptletters.ThetransliterationofPunjabiwordsintophoneticwordsisdoneusingtheGRTsystem(S.K.Singh&Sachan,2019a).TheGSTimplementationFacebookcommentsaretakenfromanarticle(S.K.Singh&Sachan,2019b),moviereviews(Pang&Lee,2004),Amazonproductreviews(Book,DVD,Electronics, Kitchen) (Blitzer et al., 2007) and large Amazon product reviews (He & McAuley,2016).Thesizeofthedatasetfortestingis172,800comments/reviews(86,400foreachEnglishandEnglish_Punjabilanguage).

2.2. Conversion in Lower Case and TokenizationTheproposedsystemusesvariousdictionariesinwhichdataarestoredinlowercase,sothetestingdatasetmustbeconvertedintolowercase.Thecommentsaresplitintosentences,andsentencesintowordsbecausetheproposedsystemworksatthewordlevel.Theimportanceoftokenization(splitting)isdiscussedinthearticle(P.K.Singhetal.,2015).

2.3. Spell Checker of Bilingual TextThetestingdatasetisgeneratedbyinsertingthePunjabi(Phonetic)wordswithintheEnglishlanguagesentence,andonlycorrectPunjabiwordswithoutspellingmistakesareused.So,thereisnoneedtocheckthespellingofPunjabiwords.ThemisspelledwordisdetectedandcorrectedusingtheexistingsystemforEnglishwords(S.K.Singh&Sachan,2019b).

Figure 1. Proposed system framework for code-mixed bilingual phonetic text


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2.4. PoS TaggingPython’sNLTKtoolisusedtotagPart-of-speech(POS)alongwitheachword.ThePOStaggingmakesiteasytoidentifytheverb,adverb,adjective(Turney,2002)fromthesentence.

2.5. elimination of Stop Words from the Bilingual TextThestopwordsarethosewordsthatareuselessforsomenaturallanguageprocessingtasks.Theusesofstopwordsdependupontaskswherethesewordswillbeused.Thesignificanceofstopwordsremovalandvariousmethodstoeliminatesuchwordsareexplainedinthearticle(Saifetal.,2014).Afterremovingthesewords,theremainingwordsarefurtherprocessed.Thereare217stopwordsinthedictionary,whichinclude134Englishstopwordsfromthearticle(S.K.Singh&Sachan,2019b)and83selectedPunjabistopwordsfromthearticle(Kaur&Saini,2016).

2.6. StemmingStemmingistheprocessoffindingtherootwordfromitsinflectionalform.Duringthestemming,generallythesuffixesandprefixesareremovedfromitsinflectionalformtogettherootword(Jivani,2011).Forexample-keeps, kept, keepingaretheinflectional(grammatical)formofthe‘keep’word.Here,therootword‘keep’isfoundusingtheremovalofsuffixesfromkeepsandkeepingwords.Thesizeofthedatabasebecomesbulkywhenallformsofawordarestored.Hence,thedatabasestoresonlyrootwords.Therefore,averbstemmersystemisdevelopedforEnglishlanguagetext,asshowninFigures2and3,inalgorithms1and2.ThestemmingisdoneonlyforverbsofEnglishlanguagewordsbecauseVODcontainsonlyopinionverbs.StemVerbalgorithm1isusedtoextracttherootverbwordfromadifferentformofaverb.ThePOStaggersareattachedwitheachword,andidentifiedallverbsforstemming.Therearethreesub-stemmingfunctionsbasedonverbforms,suchasextractionfrom(a)‘ing’,(b)“ies,es,s”and(c)pastandpastparticiple(ied,ed,en,d,n)verbform.Algorithm 1: StemVerbInput: Word along with POS tagOutput: Word (root/original word)Step 1: If POS tag are VBP, VBG, VBZ, VBN and VBD, then word is a verb Step 2: (a) if word’s last letters are ‘ing’ then call stemming function to find root word from ‘ing’ verb form (shown in Figure 2) (b) if word’s last letters are ‘ies’, ‘es’, ‘s’, then call stemming function to find root word from ‘ies’, ‘es’, ‘s’ verb form (shown in Figure 3) (c) if word’s last letters are ‘ied’, ‘ed’, ‘en’, ‘d’, ‘n’ or VBN/VBD POS tag then call stemming function to find root word from past and past participle verb form (algorithm 2)

Table 2. Testing datasets

Sr. No. Datasets Size Language

Existing Generated

1 GSTImplementation 400comments English EnglishandPunjabi

2 MovieReviews 2000reviews English EnglishandPunjabi

3 AmazonProductReviews 8000reviews English EnglishandPunjabi

4 LargeAmazonProductReviews 76,000reviews English EnglishandPunjabi


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Step 3: return word (root word/original word)Step 4: Otherwise, word in not verbStep 5: Return word.

Theprocesstoextractrootverbfrom‘ing’verbformisshowninFigure2andexplainedwithsuitableexamples.

Example-1:W=Word‘keeping’istheinflectedformof‘keep’.TheWcontains‘ing’charactersattheend,soremovethe‘ing’charactersfromWandnowcheckW(keep)existinrootverblist(RVL),yesthenreturnedW(keep)tocallingalgorithm1.Example-2:W=Word‘lying’istheinflectedformof‘lie’.TheWcontains‘ing’charactersattheend,soremovethe‘ing’charactersfromWandnowcheckW(ly)existinRVL;noWdoesnotexistinRVL.Thencheck‘y’isthelastcharacterofW(ly),yessoreplace‘y’with‘ie’,andnowWis‘lie’,againcheckinRVL,WexistsinRVL.Finally,returnedW(lie)tocallingalgorithm1.

Example-3:W=Word‘getting’istheinflectedformof‘get’.TheWcontains‘ing’charactersattheend,soremovethe‘ing’charactersfromWandnowcheckW(gett)existinRVL,noWdoesnotexistinRVL.Thencheck‘y’isthelastcharacterofW(gett),nosochecklasttwocharactersaresameandnotvowels,yesnowremoveonelastcharacterfromW(gett).Finally,Wis‘get’word,whichisreturnedtocallingalgorithm1.

Example-4:W=Word‘choosing’istheinflectedformof‘choose’.TheWcontains‘ing’charactersattheend,soremovethe‘ing’charactersfromWandnowcheckW(choos)existinRVL;noWdoesnotexistinRVL.Thencheck‘y’isthelastcharacterofW(choos),nosochecklasttwocharactersaresameandnotvowels,nonowadd‘e’attheendofW(choos).Finally,Wis‘choose’,check-inRVL,WexistsinRVL.Therefore,returnedW(choose)tothecallingalgorithm1.

Figure3showstheprocesstoextracttherootverbwordfrom“ies,es,s”verbformandexplainedwithsuitableexamples.

Example-5:W=word‘applies’isaninflectedwordof‘apply’.TheWcontains‘ies’charactersattheend.Therefore,‘ies’charactersarereplacedwith‘y’,andW(apply)ischeckedinRVL,yesWexistinRVL,sofinallyW(apply)isreturnedtocallingalgorithm1.

Example 6:W=word‘bashes’isaninflectedwordof‘bash’.TheWcontains‘es’charactersattheend.Thenremove‘es’fromtheendofW(bashes)andcheckedW(bash)inRVL;yesWexistinRVL.Therefore,finallyreturnedWtocallingalgorithm1.

Example 7:W=word‘awakes’isaninflectedwordof‘awake’.TheWcontains‘es’charactersattheend.Thenremove‘es’fromtheendofW(awakes)andcheckedW(awak)inRVL;noWdoesnotexistinRVL.Nowremove‘s’fromW(awakes)andcheckW(awake)inRVL;yesWexistinRVL.Finally,W(awake)isreturnedtocallingalgorithm1.

Algorithm 2: Stemming verb from past and past participle verb formAbbreviations Used: W=word, RVL= root verb list, WT

= target wordInput: Word along with POS taggerOutput: Word (Root/Original word)Step 1: If word’s POS tag (VBN/VBD) and exist in an irregular verb form list, then extract the root verb word from the irregular verb list. Step 2: If ‘ied’ last letter of W, then W

T=W minus ‘ied’

and add ‘y’ at end of WT

(a) If WT exist in RVL, then return W

T

(b) Otherwise return WStep 3: If ‘ed’/ ‘en’ last letter of W, then W

T= W minus


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Figure 2. Extraction of root verb from ‘ing’ verb form

Figure 3. Extraction of root verb from (ies, es, s) verb form


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‘ed’/ ‘en’ and check WT in RVL

(a) If WT exist in RVL, then return W

T

(b) If ‘d’/ ‘n’ last letter of W, then WT= W minus ‘d’/ ‘n’

and check WT in RVL

(i) If WT exist in RVL, then return W

T

(c) If last two letter of WT is same, then

(i) If ‘en’ is last letter of W, then remove one last letter from W

T

(1) If WT exist in RVL, then return W

T

(2) Otherwise, add ‘e’ at end of WT and return W

T

(ii) Otherwise, remove one last letter from WT

(1) If WT exist in RVL, then return W

T

(2) Otherwise, return WStep 4: Otherwise, return W

2.7. Sentiment CalculationInthissection,thesentimentscoreofeachsentenceiscalculated.Somenegationhandlingsrulesareproposedtodeterminethepolarityofsentencesorwords,whenevernegationwords,negativeprefixes,andpositivewordsoccurwithinthesentence.

2.7.1 Negation HandlingThesignificanceofnegationwordswasfirsttimediscussedby(Polanyi&Zaenen,2006).Thesixnegationhandlingrulesareproposedandareusedwhenthepolarityofwordschangesduetothepresence of negation words1 or negative prefixes in the sentence (P. K. Singh et al., 2015). Forexample-Idonotlikethismovie.Here‘not’isanegationwordduetowhichthepolarityofthesentencechangesfrompositivetonegative.Thresholdnegative ( )Tn valueisfixedforallnegationwords,whichisequalto-0.25discussedinthearticle(S.K.Singh&Sachan,2019b).Thenegativeprefixes(dis,de,mis,ir, il, in,un)orsuffixesarealsoactingasnegationwords,sothenegationhandlingrules(showninTable5)areusedtocalculatethesentimentscorewheneverthesenegationwordsandnegativeprefixesorsuffixesoccurwithinasentence(S.K.Singh&Sachan,2019b).All14negationwords,7negativeprefixesofEnglishlanguagediscussedinthearticle(S.K.Singh&Sachan,2019b)and8negationwords(showninTable3),4negativeprefixes(de,ka,da,a)ofPunjabilanguageareusedfortheimplementationoftheproposedsystem.

Asentence’ssentimentisexpressedbytheopinionwords(positivewords,negationwords,andverbs)presentwithinthesentence.Thepositivewordsaretheopinionwords,buttheyarenotopinionverbs.Forexample-Nicephoto.Here‘nice’wordisnotaverb,butstillexpressesthesentimentofthesentence.Therefore,thesekindsofopinionwordsareconsideredaspositivewords(S.K.Singh&Sachan,2019b).Thereareatotalof35positivewords,outofwhich20wordsofEnglisharetakenfromthearticle(S.K.Singh&Sachan,2019b)and15wordsofPunjabilanguage(showninTable4).Thesentimentscorevalueforthesepositivewordsisdenotedasthresholdpositive( )Tp ,whichisequalto0.125valueasdiscussedby(S.K.Singh&Sachan,2019b).

2.7.2 Calculation of Sentiment ScoreThe VOD consists of opinion verbs of both languages (English and Punjabi) and the sentimentscorevalue.Thevalueofthesentimentscoreisfrom-1to1.ThenegationandpositivewordsarenotincludedinthisVODbecausetheymaybeadverbs,adjectives,determiners,andprepositions.Thereare1330opinionverbs,outofwhich677Englishwordsand553wordsofPunjabilanguage.Thesentimentscoretothesewordsisassignedmanuallybasedontheirpolarityandwiththehelpof


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existingEnglishSentiWordNet3.0(Baccianellaetal.,2010).ThisVODisdomain-dependent,butitcanbefurtherusedinotherdomainsonlyafteraddingsomewordsfromthatdomain.

Theopinionverbsareconsideredasafeatureandareextractedfromthesubjectivesentences;andsearchedintotheVOD,ifthewordispresentinVOD,thenextractthevalueofsentimentscoreoftheopinionverbwhichisdenotedasWordscore .ThevalueofWordscore ispositive(Wordscore > 0 )thenthewordispositive,otherwisethevalueofWordscore isnegative(Wordscore < 0 ),thentheword

Table 3. Negation words of Punjabi language

Sr. No. Negation Word Sr. No. Negation Word

1 naahi 5 bekaar

2 nahiin 6 galat

3 viruddh 7 gair

4 bura 8 nakaaraatamak

Table 4. Positive words of Punjabi language

Sr. No. Positive Word Sr. No. Positive Word Sr. No. Positive Word

1 haan 6 bharosa 11 piaar

2 thiik 7 changa 12 sahaaita

3 dhannavaad 8 shaanadaar 13 kadar

4 khair 9 vadhiia 14 vadhaaiiaan

5 mahaan 10 sahii 15 sakaaraatamak

Table 5. Rules for Negation Handling

S.No. Opinion verb Negative Prefixes

Negation Words

Positive Word

Sentiment Score Positive/ negative

Sentiment score calculation

1 Positive Yes Yes No Positive Pos Pos Wordscore score score= +

2 Positive Yes No No Negative Neg Neg Wordscore score score= − ×1

3 Positive No Yes No Negative Neg Neg Wordscore score score= − ×1

4 No No Yes Yes Negative Neg Neg Tscore score p= − ×1

5 Verboutofthedictionary

No Yes No Negative Neg Neg Tscore score n= +

6 Negative No Yes No Positive Pos Pos Wordscore score score= − ×1


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isnegative.Thepositivesentimentscore Posscore( ) valueforallpositiveopinionwords(verb/term)iscalculatedusingEquation(1)andnegativesentimentscore( Negscore) valueforallnegativeopinionwords(verb/term)usingEquation(2)withinasentence.Thesentencesentimentscore �( )Sentscore iscalculatedusingEquation(3)inwhichthenormalizedvalueof Posscore and Negscore isadded.Tonormalizethe Posscore and Negscore , Posscore isdividedbythetotalpositiveopinionwords( p )andNegscore isdividedbythetotalnegativeopinionwords( n )respectivelyinEquation(3).Thedocument/commentsentimentscoreiscalculatedusingEquation(4),bytakingthesummationofthesentimentscorevalue �( )Sentscore .ofallthesentences,andthetotalnumberofsentencesisdenotedby‘s’inEquation(4).Thevalueofeachsentence’ssentimentscoreiscalculatedusingthe‘calculationofsentimentscore’algorithm3.

Pos Word if Wordscorei

p

score i score= >=∑0

0( ) (1)

Neg Word if Wordscorej

n

score j score= <=∑0

0( ) (2)

SentPosp

Negnscore

score score= + (3)

Doc Sentscorek

s

score k==∑0

( ) (4)

Algorithm: 3 Calculation of sentiment scoreNotations: W-> Word, VOD -> Verb Opinion Dictionary, NW-> Negation Words, NHR->Negation Handling Rules, NP-> Negative Prefixes, PWL-> Positive Word List Input: Sentence (text)Output: Sentence’s sentiment scoreStep1: Read word (W) by word from the sentence and repeat steps 2 to 5 Step2: If last W of a sentence is a NW, then add Tn to Negscore and add 1 to n, goto step 1 Step3: If W is found in VOD then extract Wordscore from VOD (a) if NW comes before W, then update PosScore or Negscore using NHR and add 1 to p/n, goto step 1 (b) otherwise, update PosScore or Negscore using Equation (1) or (2) and add 1 to p/n, goto step 1 Step4: If W is found in PWL, then


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(a) if NW comes before W, then update Negscore using NHR and add 1 to n, goto step 1 (b) otherwise, update PosScore by adding Tp and add 1 to p, goto step 1 Step5: If W does not find in VOD and PWL, then (a) if NW comes before W, then update Negscore NHR and add 1 to n, goto step 1 (b) if NP exist in W and root word found in VOD, then extract Wordscore from VOD (i) if NW comes before W, then update PosScore using NHR and add 1 to p, goto step 1 (ii) otherwise, update NegScore using NHR and add 1 to n, goto step 1 Step6: Sentscore is updated using Equation (3)Step7: Sentscore is return

2.8. document ClassificationThedocumentisclassifiedintotwoclasses(negativeorpositive)usingbinaryclassclassification.Thedocument’ssentimentscore(Docscore )iscalculatedusingEquation(4),andthedocumentisclassifiedusingarule-basedclassifierEquation(5).Thedocumentisclassifiedasapositiveclassifits’ Docscore valueisgreaterthanorequaltozero;otherwise,anegativeclassusingEquation(5).ThosedocumentswhoseDocscore valueisequaltozeroisconsideredpositiveduetothepresenceofpositivewordsinmostcasesinplaceofanopinionverb(S.K.Singh&Sachan,2019b).

DocumentPositive if DocNegative otherwise

score=≥

0 (5)

3. eXPeRIMeNTAL ReSULTS ANd dISCUSSIoN

TheproposedSAsystem’sperformanceisevaluatedforEnglishandEnglish_Punjabi(bilingual)textonfourdifferentdatasets(discussedinsection2.1).Theproposedsystemusedadictionary-basedapproachandvariousdictionariessuchasstopwords,negativeprefixes,negationwords,positivewordsofbothEnglishandPunjabilanguage.TheTn = −0 25. isthethresholdnegativevalueandTp = 0 125. is the thresholdpositivevaluewereused(S.K.Singh&Sachan,2019b).Also, theperformanceoftheproposedStemVerbsystemisdiscussedinthissection.

The system performance is measured using performance metrics such as recall, precision,accuracy,andFscore(Equation6to9).Its’somerelatedtermsaredefinedastruepositive(Tp ):positivetextsarepredictedaspositive;falsepositive( Fp ):negativetextsarepredictedaspositive;truenegative(Tn ):negativetextsarepredictedasnegativeandfalsenegative( Fn ):positivetextsarepredictedasnegative.


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RecallT

T Fp

p n

=+( )

(6)

PrecisionT

T Fp

p p

=+( )

(7)

AccuracyT T

T F F Tp n

p p n n

=+( )

+ + +( )

(8)

F score recall precisionrecall precision

� =× ×

+2 (9)

3.1 Performance of System evaluation on different datasetsTheperformanceof theproposedsystemisdiscussedin thissectiononfourdifferentdataset(s).Alldataset(s)sizeand its’domainaredifferent.Thesystem’sperformance isbetteron theGSTimplementationdataset thanotherdatasets (shown inFigures4 and5).Theperformanceof theproposedsystemisbetterintheEnglishthantheEnglish_Punjabilanguagedatasets.

Theproposedsystemisdesignedforcode-mixedbilingualphonetictext,butitsperformanceisevaluatedonbothmonolingual(English)andbilingual(English_Punjabi)text.AsperTable6,thesystemperformanceisbetterthantheexistingstate-of-the-artmethodsonalldatasets.Thesystemachievedbetterperformanceintermsofaccuracyintherangeof3%to33.38%onsocialmediadataset,1.5%to3.8%onmoviereviews,1.35%to13.81%onAmazonproductreviews,4.56%to7.34%onlargeAmazonproductreviewsformonolingualtext.Inthebilingualtext,systemperformanceisbetterthanotherexistingmethodsintermsofFscore(13.22%onsocialmediaand17.03%onmoviereviews).

3.2 Performance evaluation of StemVerb SystemAStemVerbsystemisproposedtoextracttherootverbfromtheinflectionalformofverbs.TheframeworkandworkingprocessoftheStemVerbsystem(discussedinsection2.6).Therootverblistof3582wordsandirregularverbformsalistof222wordsisgeneratedwithdifferentresources2,3,.Thereare3992wordsusedtotesttheStemVerbsystemandtheexistingtwoalgorithmsperformance,suchasSnowball4andLancaster5.TheStemVerbsystemistheonlyproposedfortheverboftheEnglishlanguage.TheperformanceofStemVerbisbetter thanSnowballandLancasterstemmer(showninFigure6).

TheperformanceofSAsystemevaluatedintermsofaccuracyforbilingualtextusingallthreestemmers,butSAsystemperformedbetterusingtheStemVerbsystemthantheothertwoexistingstemmers(showninFigure7).Also,theaccuracyoftheproposedSAsystemhasbeenimprovedafterthestemmingofverbsonbilingualdatasets(showninFigure8).TheVODcontainsonlyrootverbsalongwiththeirsentimentscores.Therefore,thestemmingofverbwordsimprovestheperformanceoftheproposedSAsystem.


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4. CoNCLUSIoN ANd FUTURe SCoPe

Thesentimentanalysissystemforbilingualcode-mixedphonetictext isdevelopedandtestedonfourdatasets,writteninbothmonolingualandbilingual.TheexistingmonolingualtextinEnglishis converted into bilingual (English and Punjabi) code-mixed phonetic text. The spell checker,VOD,negationwords,positivewords,negativeprefixes,negationhandlingrules,stopwords,andStemVerbsystemisdevelopedandused toextract thewriter’ssentimentfromtheirwritten text.ThedevelopedVODforEnglishandPunjabilanguage,contains1330verbopinionwordsofboth

Figure 4. Performance of system on the different dataset(s) (English)

Figure 5. Performance of system on the different datasets (English and Bilingual)


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Table 6. Experimental results are compared with state-of-the-art

Authors Method POS Dataset Language Accuracy (%)

F Score (%)

(Taboadaetal.,2011) Lexicon-based

Adjective,adverb,verb,noun

Moviereviews English 70.10

(Karamibekr&Ghorbani,2012)

Dictionary-based

Verb,adverb,adjective Socialmedia English 65.00

(Iqbaletal.,2015) BATwithLexicon-based Moviereviews English 69.00

(P.K.Singhetal.,2015)

Dictionary-based Verb Socialmedia English 79.00

(Shamsudinetal.,2016)

Dictionary-based

Verb,adverb,adjective Socialmedia Malay 52.12

(Bhargavaetal.,2016) SentiWordNet Moviereviews English_Hindi 54.40

(Cruzetal.,2017) Dictionary-based

Adjective,noun Moviereviews English 66.00

(Ruder&Plank,2018)

Multi-tasktri-training

Amazonproductreviews English 79.15

(Hanetal.,2018) Dictionary-based

Amazonproductreviews English 69.52 69.60

(BaoxinWang,2018)

DisconnectedRecurrentNeuralNetworks

LargeAmazonproductreviews English 64.43

(Yu&Liu,2018)

SlicedRecurrentNeuralNetworks

LargeAmazonproductreviews English 61.65


Dictionary-based Verb Socialmedia English 82.50 87.04


Dictionary-based Verb Moviereviews English 71.30 71.39

(Patwaetal.,2020) XLM-R Socialmedia English_Hindi 75.00

(Kansaletal.,2020) Logisticregression

Amazonproductreviews English 81.98

Proposed system Dictionary-based Verb

SocialmediaEnglish 85.50 89.18

English_Punjabi 84.25 88.22

MoviereviewsEnglish 72.80 73.12


Amazonproductreviews

English 83.33 83.27


LargeAmazonproductreviews

English 68.99 68.07



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EnglishandPunjabilanguage.Thelistofirregularverbsof222wordsandrootverblistof3582words isdeveloped to improve theSAsystem’sperformance.TheSAsystemclassifies text intonegativeorpositiveclassusingarule-basedclassifierandsentimentscorevalueextractedfromtheVOD.TheSAsystemobtainedanaccuracyof85.5%and84.25%ontheGSTdataset;72.8%and71%onthemoviereviews;83.33%and80.87%onAmazonproductreviews;68.99%and65.34%onlargeAmazonproductreviewsformonolingualandbilingualtextrespectively.Thissystemwillautomaticallyclassifycustomers’feedbackintopositiveandnegativeclass,andwillhelpcustomerstodecidebeforepurchasingaproduct;manufacturerscanenhancethequalityofproductandservices

Figure 6. Performance of stemmers (Snowball, Lancaster, and StemVerb)

Figure 7. Sentiment analysis system’s accuracy using all three stemmers


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basedonit,andcankeepaneyeontheircompetitors;knowthemoodofthepublicbeforeelectionandgovernmentpolicies.TheproposedSAsystemforbilingualtextcanbefurtherextendedtoothercode-mixedbilingualtextsbymodifyingsomecomponentseasily.ThedevelopedVODcanalsobeextendedforotherpart-of-speechandtootherlanguages.

Figure 8. Sentiment analysis system’s accuracy with and without stemming


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Shailendra Kumar Singh is currently a Research Scholar in the Department of Computer Science and Engineering at Sant Longowal Institute of Engineering and Technology (SLIET), Sangrur, Punjab, India. He obtained his B.Tech degree in Information Technology from UPTU (Lucknow) in 2012 and Master of Engineering in Software Engineering from Birla Institute of Technology (BIT), Mesra, Ranchi (India). He has qualified national level exams (GATE-13 & UGC-NET-2018, 2019). His research interests include handwriting recognition, sentiment analysis, natural language processing, data mining, human stress level detection and personality detection.

Manoj Kumar Sachan (PhD) is currently a Professor at Sant Longowal Institute of Engineering and Technology (SLIET), India. He is associated with the Department of Computer Science and Engineering. He did his B.Tech in Computer Science from Punjabi University, Patiala, India. He did M.E in Computer Science from Thapar Institute of Engineering & Technology, Patiala, and Ph.D from Punjab Technical University, Jalandhar, India. His research interests include handwriting recognition, stress detection, opinion mining, medical image processing, natural language processing, and data mining.

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eNdNoTeS

1 Negationwordsreversethepolarityofthewordorsentence,ifthesewordsappearbeforenegativeorpositivewordinthesentence.

2 https://www.worldclasslearning.com/english/five-verb-forms.html3 https://www.enchantedlearning.com/wordlist/verbs.shtml4 https://snowballstem.org/demo.html5 https://text-processing.com/demo/stem/

Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis

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