Random Kitchen Sink method uses a kernel function to map data vectors to a space where linear separation is possible.
ABSTRACTIVE
explicitly maps data vectors to a space where linear separation is possible, RKS method provides an approximate kernel function via explicit mapping
EXTRACTIVE
RKS approach proposed in BIBREF21, BIBREF22, explicitly maps data vectors to a space where linear separation is possible. It has been explored for natural language processing tasks BIBREF23, BIBREF24. The RKS method provides an approximate kernel function via explicit mapping.
RKS approach proposed in BIBREF21, BIBREF22, explicitly maps data vectors to a space where linear separation is possible. It has been explored for natural language processing tasks BIBREF23, BIBREF24. The RKS method provides an approximate kernel function via explicit mapping. Here, $\phi (.)$ denotes the implicit mapping function (used to compute kernel matrix), $Z(.)$ denotes the explicit mapping function using RKS and ${\Omega _k}$ denotes random variable .
2001.03131
Introduction </s> In Ġthis Ġdigital Ġera , Ġonline Ġdiscussions Ġand Ġinteractions Ġhas Ġbecome Ġa Ġvital Ġpart Ġof Ġdaily Ġlife Ġof Ġwhich Ġa Ġhuge Ġpart Ġis Ġcovered Ġby Ġsocial Ġmedia Ġplatforms Ġlike Ġtwitter , Ġfacebook , Ġinst agram Ġetc . ĠSimilar Ġto Ġreal Ġlife Ġthere Ġexist Ġanti - social Ġelements Ġin Ġthe Ġcy bers pace , Ġwho Ġtake Ġadvantage Ġof Ġthe Ġanonymous Ġnature Ġin Ġcyber Ġworld Ġand Ġindulge Ġin Ġvulgar Ġand Ġoffensive Ġcommunications . ĠThis Ġincludes Ġbullying , Ġtrolling , Ġharassment ĠB IB REF 0 , ĠB IB REF 1 Ġand Ġhas Ġbecome Ġa Ġgrowing Ġconcern Ġfor Ġgovernments . ĠYouth Ġexperiencing Ġsuch Ġvictim ization Ġwas Ġrecorded Ġto Ġhave Ġpsychological Ġsymptoms Ġof Ġanxiety , Ġdepression , Ġloneliness ĠB IB REF 1 . ĠThus Ġit Ġis Ġimportant Ġto Ġidentify Ġand Ġremove Ġsuch Ġbehaviours Ġat Ġthe Ġearliest . ĠOne Ġsolution Ġto Ġthis Ġis Ġthe Ġautomatic Ġdetection Ġusing Ġmachine Ġlearning Ġalgorithms . </s> Detect ing Ġoffensive Ġlanguage Ġfrom Ġsocial Ġmedia Ġis Ġa Ġchallenging Ġresearch Ġproblem Ġdue Ġto Ġthe Ġdifferent Ġlevel Ġof Ġambig u ities Ġpresent Ġin Ġthe Ġnatural Ġlanguage Ġand Ġnoisy Ġnature Ġof Ġthe Ġsocial Ġmedia Ġlanguage . ĠMoreover , Ġsocial Ġmedia Ġsubscribers Ġare Ġfrom Ġlingu istically Ġdiverse Ġand Ġvarying Ġcommunities . ĠOver seeing Ġthe Ġcomplication Ġof Ġthis Ġproblem , ĠB IB REF 2 Ġorganized Ġa Ġtask Ġin ĠSem E val 2019 , ĠTask Ġ6 : ĠIdent ifying Ġand ĠC ategor izing ĠOffensive ĠLanguage Ġin ĠSocial ĠMedia . ĠThe Ġtweets Ġwere Ġcollected Ġby Ġthe Ġorganizers Ġusing ĠTwitter ĠAPI Ġand Ġhave Ġannot ated Ġthem Ġin Ġa Ġhierarchical Ġmanner Ġas Ġoffensive Ġlanguage Ġpresent Ġin Ġthe Ġtweet , Ġtype Ġof Ġthe Ġoffense Ġand Ġtarget Ġof Ġthe Ġoffense . ĠThere Ġwere Ġthree Ġsub - t asks Ġaccording Ġto Ġthe Ġhierarchy Ġof Ġannotation : Ġa ) ĠTo Ġdetect Ġif Ġa Ġpost Ġis Ġoffensive Ġ( OFF ) Ġor Ġnot Ġ( NOT ), Ġb ) ĠTo ĠIdent ify Ġthe Ġtype Ġof Ġoffense Ġin Ġthe Ġpost Ġas Ġtargeted Ġthreat Ġ( T TH ), Ġtargeted Ġinsult Ġ( T IN ), Ġunt arget ed Ġ( UN T ), Ġc ) ĠTo Ġidentify Ġif Ġoffense Ġis Ġtargeted Ġto Ġorganization Ġor Ġentity Ġ( OR G ), Ġgroup Ġof Ġpeople Ġ( GR P ), Ġindividual Ġ( IND ), Ġor Ġother Ġ( OTH ). </s> The Ġdataset Ġhad Ġthe Ġfollowing Ġchallenges : </s> Dat as et Ġwas Ġcomparatively Ġsmaller . </s> Dat as et Ġwas Ġbiased / im balanced ĠB IB REF 3 . </s> In Ġthis Ġpaper , Ġwe Ġare Ġproposing Ġa Ġcomparative Ġanalysis Ġfor Ġthe Ġsub - task ĠA Ġ: Off ensive Ġlanguage Ġidentification Ġof Ġthe ĠSem E val 2019 , ĠTask Ġ6 . ĠSub - task ĠA Ġwas Ġthe Ġmost Ġpopular Ġsub - task Ġamong Ġthe Ġthree Ġand Ġhad Ġtotal Ġ104 Ġteam Ġparticipation . ĠIn ĠTable ĠT AB REF 19 , Ġthe Ġlist Ġof Ġfirst Ġ5 Ġparticipants Ġalong Ġwith Ġsystem Ġand Ġf 1 - score Ġhas Ġbeen Ġshown . </s> Off ensive Ġlanguage Ġdetection Ġis Ġone Ġof Ġthe Ġchallenging Ġand Ġinteresting Ġtopic Ġfor Ġresearch . ĠRecent Ġpast Ġhad Ġmultiple Ġshared Ġtasks Ġand Ġresearch Ġon Ġthis Ġtopic . ĠOne Ġof Ġthe Ġinitial Ġwork Ġon Ġoffensive Ġlanguage Ġusing Ġsupervised Ġclassification Ġwas Ġdone Ġby ĠYin Ġet Ġal . ĠB IB REF 4 . ĠThey Ġhave Ġused ĠN gram , ĠTF ID F Ġand Ġcombination Ġof ĠTF ID F Ġwith ĠSent iment Ġand ĠContext ual Ġas ĠFeatures . ĠSchmidt Ġand ĠW ieg and ĠB IB REF 5 Ġgave Ġa Ġsurvey Ġon Ġautomatic Ġhate Ġspeech Ġdetection Ġusing ĠN LP . ĠThe Ġauthors Ġsurveyed Ġon Ġfeatures Ġlike ĠSimple ĠSurface ĠFeatures , ĠWord ĠGeneral ization , ĠL ingu istic ĠFeatures , ĠKnowledge - Based ĠFeatures , ĠMult im od al ĠInformation Ġetc . ĠIn Ġ2013 , Ġa Ġstudy Ġon Ġdetecting Ġcyber bull ying Ġin ĠYouTube Ġcomments Ġwas Ġdone Ġby ĠDad var Ġet Ġal . ĠB IB REF 6 . ĠThey Ġhave Ġused Ġa Ġcombination Ġof Ġcontent - based , Ġcyber bull ying - specific Ġand Ġuser - based Ġfeatures Ġand Ġshowed Ġthat Ġdetection Ġof Ġcyber bull ying Ġcan Ġbe Ġimproved Ġby Ġtaking Ġuser Ġcontext Ġinto Ġaccount . ĠShared Ġtask ĠGerm E val Ġ2018 Ġorganised Ġby ĠW ieg and Ġet Ġal ., BI B REF 7 Ġwas Ġfocused Ġon Ġoffensive Ġlanguage Ġdetection Ġon ĠGerman Ġtweets . ĠIt Ġhad Ġa Ġdataset Ġof Ġover Ġ8 , 500 Ġannot ated Ġtweets Ġand Ġwas Ġtrained Ġfor Ġbinary Ġclassification Ġof Ġoffensive Ġand Ġnon - offensive Ġtweets . ĠThey Ġobtained Ġan Ġoverall Ġmac o - F 1 Ġscore Ġof Ġ76 . 77 %. ĠAnother Ġshared Ġtask Ġon ĠAgg ression ĠIdentification Ġin ĠSocial ĠMedia Ġwas Ġorganised Ġby ĠKumar Ġet Ġal ., ĠB IB REF 8 . ĠThe Ġtask Ġprovided Ġa Ġdataset Ġwith Ġ15 , 000 Ġannot ated ĠFacebook Ġposts Ġand Ġcomments Ġin ĠHindi Ġand ĠEnglish . ĠThey Ġobtained Ġa Ġweighted ĠF - score Ġof Ġ64 % Ġfor Ġboth ĠEnglish Ġand ĠHindi . ĠThe Ġrest Ġof Ġthe Ġpaper Ġis Ġstructured Ġas Ġfollows . ĠSection Ġ2 Ġexplains Ġabout Ġthe Ġmethodology Ġwith Ġformulation . ĠSection Ġ3 Ġdiscusses Ġon Ġthe ĠProp osed Ġapproach . ĠSection Ġ4 Ġtalks Ġon Ġthe ĠExper iments Ġand Ġdiscussions Ġperformed . ĠFinally , Ġconclusion Ġis Ġgiven Ġin ĠSection Ġ5 . </s> Method ology Ġ: :: ĠData ĠPre - processing </s> Data Ġpre - processing Ġis Ġa Ġvery Ġcrucial Ġstep Ġwhich Ġneeds Ġto Ġbe Ġdone Ġbefore Ġapplying Ġany Ġmachine Ġlearning Ġtasks , Ġbecause Ġthe Ġreal Ġtime Ġdata Ġcould Ġbe Ġvery Ġnoisy Ġand Ġun struct ured . ĠFor Ġthe Ġtwo Ġmodels Ġused Ġin Ġthis Ġwork , Ġpre - processing Ġof Ġtweets Ġis Ġdone Ġseparately : </s> Pre - processing Ġfor ĠGoogle Ġmodel : </s> It Ġhas Ġbecome Ġa Ġcommon Ġculture Ġto Ġuse Ġ# tags Ġacross Ġsocial Ġmedia . ĠSo Ġwe Ġhave Ġreplaced Ġmultiple Ġ# tags Ġwith Ġa Ġsingle Ġ# tag . ĠMostly Ġ@ Ġsymbol Ġid Ġused Ġto Ġmention Ġperson Ġor Ġentities Ġin Ġa Ġtweet . ĠSo Ġwe Ġreplace Ġmultiple Ġ@ sy mb ols Ġwith Ġa Ġsingle Ġ@ - ment ion . ĠSome Ġtweets Ġmay Ġcontain Ġthe Ġlink Ġto Ġa Ġwebsite Ġor Ġsome Ġother Ġur ls . ĠSo Ġwe Ġreplace Ġall Ġof Ġthese Ġwith Ġa Ġsingle Ġkeyword ĠUR LS . </s> Pre - processing Ġfor Ġfast text Ġmodel : </s> For Ġapplying Ġfast text Ġmodel Ġto Ġget Ġword Ġvectors , Ġwe Ġfollowed Ġa Ġdifferent Ġset Ġof Ġpre - processing Ġsteps . ĠFirst , Ġall Ġthe Ġnumbers , Ġpunct uation Ġmarks , Ġur ls Ġ( http :// Ġor Ġwww .) Ġand Ġsymbols Ġ( em oji , Ġ# tags , Ġ- ment ion ) Ġwere Ġremoved Ġfrom Ġthe Ġtweet Ġas Ġit Ġdo Ġnot Ġcontain Ġinformation Ġrelated Ġto Ġsentiment . ĠAfter Ġthat , Ġtoken ization Ġand Ġlower c asing Ġwas Ġapplied Ġto Ġthe Ġtweets . ĠToken ization Ġwas Ġdone Ġusing Ġtoken izer Ġfrom ĠNL T K Ġpackage ĠB IB REF 9 . ĠFinally , Ġthe Ġstop Ġword Ġare Ġremoved . ĠThe Ġlist Ġis Ġobtained Ġfrom ĠNL T K Ġpackage . </s> Method ology Ġ: :: ĠEmb edd ings </s> Word Ġembed d ings Ġare Ġubiquitous Ġfor Ġany ĠN LP Ġproblem , Ġas Ġalgorithms Ġcannot Ġprocess Ġthe Ġplain Ġtext Ġor Ġstrings Ġin Ġits Ġraw Ġform . ĠWord Ġem edd ings Ġare Ġvectors Ġthat Ġcaptures Ġthe Ġsemantic Ġand Ġcontextual Ġinformation Ġof Ġwords . ĠThe Ġword Ġembed ding Ġused Ġfor Ġthis Ġwork Ġare : </s> Fast Text : ĠThe Ġfast Text Ġalgorithm Ġcreated Ġby ĠFacebook ĠB IB REF 10 Ġassumes Ġevery Ġword Ġto Ġbe Ġn - gram s Ġof Ġcharacter . ĠIt Ġhelps Ġto Ġgive Ġthe Ġvector Ġrepresentations Ġfor Ġout Ġof Ġvoc ab uary Ġwords . ĠFor Ġthe Ġcurrent Ġwork , Ġfast text Ġbased Ġword Ġembed ding Ġis Ġused Ġfor Ġgenerating Ġtoken Ġvectors Ġof Ġdimension Ġ300 ĠB IB REF 11 . ĠEach Ġvector Ġcorresponding Ġto Ġthe Ġtweet Ġis Ġgenerated Ġby Ġtaking Ġthe Ġaverage Ġof Ġtoken Ġvectors . </s> Universal ĠSent ence ĠEnc oder : ĠDevelop ed Ġby ĠGoogle , ĠUniversal Ġsentence Ġenc oder ĠB IB REF 12 , ĠB IB REF 13 Ġprovides Ġembed d ings Ġat Ġsentence Ġlevel . ĠThe Ġdimension Ġof Ġthe Ġembed ding Ġvector Ġis Ġ512 , Ġirrespective Ġof Ġthe Ġnumber Ġof Ġtokens Ġin Ġthe Ġinput Ġtweet . ĠThese Ġvectors Ġcan Ġcapture Ġgood Ġsemantic Ġinformation Ġfrom Ġthe Ġsentences . ĠFor Ġeach Ġtweet , Ġthis Ġmodel Ġgenerates Ġa Ġ512 Ġlength Ġembed ding Ġvector Ġand Ġis Ġused Ġas Ġfeatures Ġfor Ġfurther Ġclassification . </s> D MD Ġand ĠH OD MD : ĠD MD Ġis Ġa Ġmethod Ġinitially Ġused Ġin Ġfluid Ġdynamics Ġwhich Ġcaptures Ġspat io - tem poral Ġfeatures ĠB IB REF 14 . ĠIt Ġhas Ġbeen Ġused Ġin Ġbackground - fore ground Ġseparation ĠB IB REF 15 , Ġload Ġforecasting ĠB IB REF 16 , Ġsal iency Ġdetection ĠB IB REF 17 Ġetc . ĠFor Ġnatural Ġlanguage Ġprocessing , ĠD MD Ġhas Ġbeen Ġfirst Ġapplied Ġfor Ġsentiment Ġanalysis ĠB IB REF 18 , ĠB IB REF 19 . ĠThis Ġmotivated Ġto Ġexplore ĠD MD Ġbased Ġfeature Ġfor Ġthe Ġpresent Ġwork . </s> Dynamic Ġmode Ġdecom position Ġ( D MD ) Ġis Ġa Ġmuch Ġmore Ġpowerful Ġconcept Ġand Ġit Ġassumes Ġthe Ġevolution Ġof Ġthe Ġfunction Ġover Ġthe Ġrectangular Ġfield Ġis Ġeffected Ġby Ġthe Ġmapping Ġof Ġa Ġconstant Ġmatrix Ġ$ A $. Ġ$ A $ Ġcaptures Ġthe Ġsystem âĢ Ļ s Ġinherent Ġdynamics Ġand Ġthe Ġaim Ġof Ġthe ĠD MD Ġis Ġto Ġunderstand Ġusing Ġits Ġdominant Ġe ign eval ues Ġand Ġe igen ve ctors . ĠAss umption Ġis Ġthat Ġthis Ġmatrix Ġ$ A $ Ġis Ġof Ġlow Ġrank Ġand Ġhence Ġthe Ġsequence Ġof Ġvectors Ġ$ Ġ\ math op Ġ{{ x _ 1 }} \ limits Ġ_ | ^ | Ġ, \ math op Ġ{{ x _ 2 }} \ limits Ġ_ | ^ | Ġ, \ math op Ġ{{ x _ 3 }} \ limits Ġ_ | ^ | Ġ, ... \ math op Ġ{ , { x _ k }} \ limits Ġ_ | ^ | Ġ, ... , \ math op Ġ{{ x _{ m Ġ+ Ġ1 }} }\ limits Ġ_ | ^ | Ġ$ Ġfinally Ġbecome Ġa Ġlin early Ġdependent Ġset . ĠThat Ġis , Ġvector Ġ$ Ġ\ math op Ġ{{ x _{ m Ġ+ Ġ1 }} }\ limits Ġ_ | ^ | $ Ġbecome Ġlin early Ġdependent Ġon Ġprevious Ġvectors . ĠThe Ġdata Ġmatrix ĠX Ġin Ġterms Ġof Ġe igen Ġvectors Ġassociated Ġwith Ġmatrix Ġ$ A $. </s> where , Ġ${ \ Ph i Ġ^ \ d ag Ġ} $ Ġis Ġpseudo Ġinverse Ġof Ġ${ \ Ph i Ġ} $. Ġ$ A $ Ġis Ġof Ġrank Ġm Ġand Ġ${ \ Ph i Ġ} $ Ġhave Ġm Ġcolumns . ĠHence , Ġpseudo - in verse Ġwill Ġdo Ġthe Ġjob Ġthan Ġinverse Ġoperation . ĠThe Ġcolumns Ġof Ġ${ \ Ph i Ġ} $ Ġare Ġcalled ĠD MD Ġmodes Ġand Ġthis Ġforms Ġthe Ġfeatures . </s> Time - l agged Ġmat rices Ġare Ġprepared Ġas Ġsnapshot Ġfor Ġthis Ġapproach . ĠIn ĠE ig ens ent ĠB IB REF 20 , Ġthe Ġauthors Ġproposed ĠH OD MD Ġto Ġfind Ġembed ings Ġfor Ġsentences . ĠThe Ġauthors Ġsuggested , Ġsentences Ġcan Ġbe Ġrepresented Ġas Ġa Ġsignal Ġusing Ġword Ġembed d ings Ġby Ġtaking Ġthe Ġaverage Ġof Ġword Ġvectors . ĠThis Ġis Ġintuitive Ġbecause Ġword Ġembed d ings Ġalmost Ġobe ys Ġthe Ġlaws Ġof Ġlinear Ġalgebra , Ġby Ġcapturing Ġword Ġanalog ies Ġand Ġrelationships . ĠTherefore , Ġby Ġconsidering Ġevery Ġsentence Ġas Ġa Ġmulti - dimensional Ġsignal , Ġwe Ġcan Ġcapture Ġthe Ġimportant Ġtransitional Ġdynamics Ġof Ġsentences . ĠAlso , Ġfor Ġthe Ġsignal Ġrepresentation Ġof Ġsentences , Ġeach Ġword Ġvector Ġwill Ġact Ġas Ġa Ġsingle Ġpoint Ġin Ġthe Ġsignal . ĠFor Ġthe Ġpresent Ġwork , Ġto Ġgenerate ĠD MD Ġand ĠH OD MD Ġbased Ġfeatures , ĠFast ext Ġbased Ġembed ding Ġis Ġused . </s> Prop osed ĠApproach </s> R KS Ġapproach Ġproposed Ġin ĠB IB REF 21 , ĠB IB REF 22 , Ġexplicitly Ġmaps Ġdata Ġvectors Ġto Ġa Ġspace Ġwhere Ġlinear Ġseparation Ġis Ġpossible . ĠIt Ġhas Ġbeen Ġexplored Ġfor Ġnatural Ġlanguage Ġprocessing Ġtasks ĠB IB REF 23 , ĠB IB REF 24 . ĠThe ĠR KS Ġmethod Ġprovides Ġan Ġapproximate Ġkernel Ġfunction Ġvia Ġexplicit Ġmapping . </s> Here , Ġ$\ phi Ġ( .) $ Ġdenotes Ġthe Ġimplicit Ġmapping Ġfunction Ġ( used Ġto Ġcompute Ġkernel Ġmatrix ), Ġ$ Z ( .) $ Ġdenotes Ġthe Ġexplicit Ġmapping Ġfunction Ġusing ĠR KS Ġand Ġ${ \ O mega Ġ_ k } $ Ġdenotes Ġrandom Ġvariable Ġ. </s> Figure ĠFIG REF 15 Ġshow Ġthe Ġblock Ġdiagram Ġof Ġthe Ġproposed Ġapproach . </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠData ĠDescription </s> OL ID Ġ( Off ensive ĠLanguage ĠIdentification ĠDat as et ) Ġis Ġa Ġcollection Ġof ĠEnglish Ġtweets Ġwhich Ġare Ġannot ated Ġusing Ġa Ġthree - level Ġhierarchical Ġannotation Ġmodel . ĠIt Ġwas Ġcollected Ġusing ĠTwitter ĠAPI Ġand Ġcontains Ġ14 , 460 Ġannot ated Ġtweets . ĠThe Ġtask Ġdivided Ġthe Ġdata Ġas Ġtrain , Ġtrial Ġand Ġtest , Ġof Ġwhich Ġtrain Ġand Ġtrial Ġwas Ġinitially Ġreleased Ġas Ġstarting Ġkit , Ġfinally Ġtest Ġwas Ġreleased Ġas ĠTest ĠA Ġrelease . ĠAll Ġthree Ġof Ġthese Ġpartitions Ġwere Ġhighly Ġbiased , Ġthus Ġmaking Ġthe Ġtask Ġmore Ġchallenging Ġand Ġreal Ġtime . ĠThe Ġtrain Ġset Ġhad Ġ13 , 240 Ġtweets , Ġout Ġof Ġwhich Ġ88 40 Ġtweets Ġwere Ġnot Ġoffensive Ġ( NOT ) Ġand Ġ44 00 Ġtweets Ġwere Ġoffensive Ġ( OFF ). ĠSimilarly , Ġtest Ġset Ġhad Ġ8 60 Ġtweets , Ġwhich Ġhad Ġ620 Ġnot Ġoffensive Ġand Ġ280 Ġoffensive Ġtweets . ĠTable TA B REF 17 Ġshow Ġthe Ġdata Ġdistribution Ġof Ġthe Ġentire Ġdataset . ĠFor Ġthe Ġcurrent Ġwork , Ġtrain Ġand Ġtest Ġdata Ġare Ġtaken Ġwhich Ġis Ġ14 , 100 Ġtweets Ġin Ġnumber . </s> In ĠSub - task ĠA : ĠOffensive Ġlanguage Ġidentification , Ġthe Ġgoal Ġwas Ġto Ġdiscriminate Ġoffensive Ġand Ġnot - offensive Ġtwitter Ġposts . ĠThe Ġtarget Ġclasses Ġfor Ġeach Ġinstance Ġwere Ġa ) ĠOffensive Ġ( OFF ): Ġposts Ġthat Ġcontain Ġany Ġform Ġof Ġprof anity Ġor Ġtargeted Ġoffence . ĠThis Ġincludes Ġthreats , Ġinsults , Ġand Ġany Ġform Ġof Ġunt arget ed Ġprof anity . Ġb ) ĠNot ĠOffensive Ġ( NOT ): Ġposts Ġthat Ġdoesn 't Ġhave Ġany Ġprof anity Ġor Ġoffense Ġin Ġit . ĠThe Ġresult Ġand Ġdiscussion Ġof Ġthe Ġtop Ġ10 Ġteams Ġfor Ġthe Ġsub - task ĠA Ġis Ġin Ġsection Ġfor ĠIntroduction . ĠIn Ġthat , Ġteam ĠB IB REF 3 Ġobtained Ġhighest Ġf 1 - score Ġof Ġ82 . 9 % </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠResults Ġand ĠCompar isons </s> This Ġsection Ġdescribes Ġthe Ġresult Ġas Ġthree Ġdifferent Ġcases . ĠCase Ġ1 Ġ& Ġ2 Ġprovides Ġbaseline Ġapproach Ġto Ġcompare Ġwith Ġthe Ġproposed ĠR KS Ġappro c ah Ġdescribed Ġin Ġcase Ġ3 . ĠTable ĠT AB REF 19 Ġgives Ġthe Ġresults Ġof Ġthe Ġtop Ġ5 Ġteams Ġof Ġsub - task ĠA . ĠTeam Ġwith Ġrank Ġ1 Ġachieved Ġa Ġmaximum Ġf 1 - score Ġof Ġ82 . 9 %. </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠResults Ġand ĠCompar isons Ġ: :: ĠCase Ġ1 : ĠEmb edd ings Ġapproach </s> In Ġthis Ġwork , Ġwe Ġhave Ġselected Ġword Ġvectors Ġgenerated Ġby ĠGoogle Ġuniversal Ġenc oder Ġmodel , ĠFast text , Ġand ĠD MD Ġbased Ġfeatures . ĠThe Ġclassification Ġusing Ġthe Ġselected Ġfeatures Ġare Ġperformed Ġusing Ġmachine Ġlearning Ġalgorithms Ġsuch Ġas ĠRandom ĠForest Ġ( RF ), ĠDecision ĠTree Ġ( DT ), ĠNa ive ĠBay es Ġ( NB ), ĠSupport Ġvector Ġmachine Ġ( S VM ) Ġlinear Ġand ĠRB F Ġkernels , ĠLog istic ĠReg ression , Ġand ĠRandom Ġkitchen Ġsinks . ĠThe Ġevaluation Ġmeasures Ġused Ġare Ġaccuracy Ġ( Acc .), Ġprecision Ġ( Pre c ), Ġrecall , Ġf 1 - score Ġ( F 1 ). ĠTable ĠT AB REF 21 Ġshows Ġthe Ġclassification Ġresult Ġobtained Ġfor Ġclassical Ġmachine Ġlearning Ġalgorithms Ġusing Ġthe ĠGoogle Ġuniversal Ġsentence Ġenc oder Ġmodel Ġfeatures . ĠIt Ġcan Ġbe Ġobserved Ġthat Ġs vm Ġlinear Ġclass ifier Ġand ĠLog istic Ġregression Ġhas Ġgiven Ġmaximum Ġaccuracy Ġof Ġ82 . 44 % Ġand Ġ82 . 56 %. </s> Table ĠT AB REF 22 Ġshows Ġthe Ġclassification Ġresults Ġobtained Ġusing Ġthe Ġfeatures Ġgenerated Ġby Ġfast text Ġmodel Ġfor Ġclassical Ġmachine Ġlearning Ġalgorithms . ĠFor Ġthe Ġfast text Ġmodel Ġalso , Ġs vm Ġlinear Ġand Ġlog istic Ġregression Ġmodel Ġhave Ġgiven Ġmaximum Ġaccur acies Ġof Ġ81 . 16 % Ġrespectively . </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠResults Ġand ĠCompar isons Ġ: :: ĠCase Ġ2 : ĠD MD Ġapproach </s> In Ġorder Ġto Ġprovide Ġa Ġcomparison , Ġwe Ġexplore ĠD MD Ġbased Ġfeatures . ĠThe ĠTable ĠT AB REF 24 Ġshows Ġthe Ġresult Ġobtained Ġfor Ġnormal ĠD MD Ġand ĠH OD MD Ġbased Ġfeature . ĠThe Ġorder Ġfor ĠH OD MD Ġfor Ġthe Ġpresent Ġwork Ġis Ġ2 Ġ& Ġ3 . ĠThe Ġclassification Ġis Ġperformed Ġusing ĠS VM - linear Ġkernel Ġwith Ġcontrol Ġparameter Ġvalue Ġchosen Ġas Ġ1000 Ġas Ġthe Ġsuitable Ġone . ĠWe Ġtried Ġfor Ġother Ġvalues Ġsuch Ġas Ġ0 . 1 , Ġ1 , Ġ100 , Ġ500 , Ġand Ġ1000 . ĠFigure ĠFIG REF 25 Ġshows Ġthe Ġcontrol Ġparameter Ġversus Ġaccuracy Ġplot Ġwhich Ġhelped Ġto Ġfix Ġthe Ġparameter Ġvalue . </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠResults Ġand ĠCompar isons Ġ: :: ĠCase Ġ3 : ĠR KS Ġapproach </s> R KS Ġapproach Ġhas Ġbeen Ġused Ġin Ġthe Ġarticles Ġfor ĠN LP Ġtasks Ġ[ 29 , 30 , 23 ]. ĠIn Ġthis Ġwork , Ġwe Ġuse ĠR KS Ġto Ġimp or ve Ġthe Ġevaluation Ġscores Ġas Ġdiscussed Ġpreviously . ĠThe ĠR KS Ġapproach Ġexplicitly Ġmaps Ġthe Ġembed ding Ġvectors Ġto Ġa Ġdimension Ġwhere Ġthe Ġdata Ġbecomes Ġlin early Ġsepar able . ĠIn Ġthat Ġspace , Ġregular ized Ġleast - square Ġbased Ġclassification Ġ( RL SC ) Ġis Ġperformed . ĠThe Ġimplementation Ġof Ġthe ĠR KS Ġis Ġtaken Ġfrom ĠB IB REF 29 , ĠB IB REF 30 . ĠThe Ġfeature Ġvectors Ġfrom ĠGoogle Ġuniversal Ġsentence Ġenc oder Ġand Ġfast text Ġare Ġexplicitly Ġmapped Ġusing ĠR KS Ġand Ġthe Ġresults Ġare Ġtab ulated Ġin ĠTable ĠT AB REF 27 Ġand ĠT AB REF 28 . </s> The ĠTable ĠT AB REF 27 Ġshows Ġthe Ġclassification Ġreport Ġon Ġthe Ġproposed ĠR KS Ġmethod Ġtaking Ġword Ġvectors Ġgenerated Ġby ĠGoogle Ġuniversal Ġenc oder Ġmodel Ġas Ġfeatures Ġwith Ġdimension Ġ512 . ĠFor Ġthis Ġwork , Ġsuch Ġvector Ġis Ġexplicitly Ġmapped Ġto Ġdimensions Ġ100 , Ġ200 , Ġ500 Ġand Ġ1000 Ġusing ĠR KS . ĠThe Ġmaximum Ġaccuracy Ġobtained Ġis Ġ90 . 58 % Ġfor Ġhigher Ġdimension Ġ1000 . </s> Table ĠT AB REF 28 Ġshows Ġthe Ġclassification Ġreport Ġon Ġthe Ġproposed ĠR KS Ġmethod Ġtaking Ġword Ġvectors Ġgenerated Ġby ĠFast text Ġmodel Ġas Ġfeatures . ĠFor Ġthis Ġmodel Ġalso , Ġfeatures Ġare Ġmapped Ġto Ġdimensions Ġ100 , Ġ200 , Ġ500 Ġand Ġ1000 . ĠFor ĠFast text Ġmodel , Ġthe Ġproposed Ġmethod Ġgave Ġa Ġmaximum Ġaccuracy Ġof Ġ99 . 53 %, Ġwhich Ġis Ġa Ġbench Ġmarking Ġresult Ġwhen Ġcompared Ġto Ġthe Ġliterature . ĠThis Ġresult Ġshows Ġthe Ġdiscriminating Ġcapability Ġof Ġthe Ġfeatures Ġchosen , Ġas Ġwhen Ġmapped Ġto Ġhigher Ġdimensions , Ġthey Ġbecome Ġlin early Ġsepar able . ĠFrom ĠTable ĠT AB REF 27 Ġand ĠT AB REF 28 Ġit Ġcan Ġbe Ġobserved Ġthat Ġas Ġthe Ġmapping Ġdimension Ġincreases , Ġthe Ġevaluation Ġscore Ġalso Ġimproves . ĠThis Ġshows Ġthe Ġeffectiveness Ġof Ġthe ĠR KS Ġapproach Ġto Ġobtain Ġcompeting Ġscore . ĠThe Ġcapability Ġof Ġthe ĠR KS Ġapproach Ġcane Ġbe Ġexplored Ġon Ġlarge Ġdatasets . </s> Conclusion </s> Off ensive Ġlanguage Ġdetection Ġis Ġan Ġimportant Ġtask Ġrelated Ġto Ġsocial Ġmedia Ġdata Ġanalysis . ĠThe Ġnature Ġof Ġthe Ġcontent Ġcan Ġvary Ġas Ġits Ġprovided Ġby Ġdifferent Ġpeople . ĠThe Ġcurrent Ġwork Ġuses Ġthe Ġdata Ġprovided Ġin ĠSem E val Ġ2019 Ġshared Ġtask ĠA Ġfor ĠOffensive Ġlanguage Ġidentification . ĠA Ġcomparative Ġstudy Ġis Ġprovided Ġby Ġexploring Ġthe Ġeffectiveness Ġof ĠGoogle Ġuniversal Ġsentence Ġenc oder , ĠFast text Ġbased Ġembed ding , ĠDynamic ĠMode ĠDec om position Ġbased Ġfeatures Ġand ĠR KS Ġbased Ġexplicit Ġmapping Ġapproach . ĠFor Ġthe Ġexperiments , Ġwe Ġused Ġthe Ġmachine Ġlearning Ġmethods Ġsuch Ġas ĠS VM Ġlinear , ĠRandom ĠForest , ĠLog istic Ġregression , ĠNav ie ĠBay es Ġand ĠRegular ized Ġleast - square Ġbased Ġclassification . ĠThe Ġmeasures Ġused Ġfor Ġevaluation Ġare Ġaccuracy , Ġprecision , Ġrecall , Ġand Ġf 1 - score . ĠWe Ġobserved Ġthat ĠR KS Ġapproach Ġimproved Ġthe Ġresults . ĠHowever , Ġas Ġa Ġfuture Ġwork , Ġthe Ġproposed Ġapproach Ġcane Ġbe Ġexplored Ġon Ġlarge Ġdatasets .
Introduction In this digital era, online discussions and interactions has become a vital part of daily life of which a huge part is covered by social media platforms like twitter, facebook, instagram etc. Similar to real life there exist anti-social elements in the cyberspace, who take advantage of the anonymous nature in cyber world and indulge in vulgar and offensive communications. This includes bullying, trolling, harassment BIBREF0, BIBREF1 and has become a growing concern for governments. Youth experiencing such victimization was recorded to have psychological symptoms of anxiety, depression, loneliness BIBREF1. Thus it is important to identify and remove such behaviours at the earliest. One solution to this is the automatic detection using machine learning algorithms. Detecting offensive language from social media is a challenging research problem due to the different level of ambiguities present in the natural language and noisy nature of the social media language. Moreover, social media subscribers are from linguistically diverse and varying communities. Overseeing the complication of this problem, BIBREF2 organized a task in SemEval2019, Task 6: Identifying and Categorizing Offensive Language in Social Media. The tweets were collected by the organizers using Twitter API and have annotated them in a hierarchical manner as offensive language present in the tweet, type of the offense and target of the offense. There were three sub-tasks according to the hierarchy of annotation: a) To detect if a post is offensive (OFF) or not (NOT), b) To Identify the type of offense in the post as targeted threat (TTH), targeted insult (TIN), untargeted (UNT), c) To identify if offense is targeted to organization or entity (ORG), group of people (GRP), individual (IND), or other (OTH). The dataset had the following challenges: Dataset was comparatively smaller. Dataset was biased/imbalanced BIBREF3. In this paper, we are proposing a comparative analysis for the sub-task A :Offensive language identification of the SemEval2019, Task 6. Sub-task A was the most popular sub-task among the three and had total 104 team participation. In Table TABREF19, the list of first 5 participants along with system and f1-score has been shown. Offensive language detection is one of the challenging and interesting topic for research. Recent past had multiple shared tasks and research on this topic. One of the initial work on offensive language using supervised classification was done by Yin et al. BIBREF4. They have used Ngram, TFIDF and combination of TFIDF with Sentiment and Contextual as Features. Schmidt and Wiegand BIBREF5 gave a survey on automatic hate speech detection using NLP. The authors surveyed on features like Simple Surface Features, Word Generalization, Linguistic Features, Knowledge-Based Features, Multimodal Information etc. In 2013, a study on detecting cyberbullying in YouTube comments was done by Dadvar et al. BIBREF6. They have used a combination of content-based, cyberbullying-specific and user-based features and showed that detection of cyberbullying can be improved by taking user context into account. Shared task GermEval 2018 organised by Wiegand et al.,BIBREF7 was focused on offensive language detection on German tweets. It had a dataset of over 8,500 annotated tweets and was trained for binary classification of offensive and non-offensive tweets. They obtained an overall maco-F1 score of 76.77%. Another shared task on Aggression Identification in Social Media was organised by Kumar et al., BIBREF8. The task provided a dataset with 15,000 annotated Facebook posts and comments in Hindi and English. They obtained a weighted F-score of 64% for both English and Hindi. The rest of the paper is structured as follows. Section 2 explains about the methodology with formulation. Section 3 discusses on the Proposed approach. Section 4 talks on the Experiments and discussions performed. Finally, conclusion is given in Section 5. Methodology ::: Data Pre-processing Data pre-processing is a very crucial step which needs to be done before applying any machine learning tasks, because the real time data could be very noisy and unstructured. For the two models used in this work, pre-processing of tweets is done separately: Pre-processing for Google model: It has become a common culture to use #tags across social media. So we have replaced multiple #tags with a single #tag. Mostly @ symbol id used to mention person or entities in a tweet. So we replace multiple @symbols with a single @-mention. Some tweets may contain the link to a website or some other urls. So we replace all of these with a single keyword URLS. Pre-processing for fasttext model: For applying fasttext model to get word vectors, we followed a different set of pre-processing steps. First, all the numbers, punctuation marks, urls (http:// or www.) and symbols (emoji, #tags, -mention) were removed from the tweet as it do not contain information related to sentiment. After that, tokenization and lowercasing was applied to the tweets. Tokenization was done using tokenizer from NLTK package BIBREF9. Finally, the stop word are removed. The list is obtained from NLTK package. Methodology ::: Embeddings Word embeddings are ubiquitous for any NLP problem, as algorithms cannot process the plain text or strings in its raw form. Word emeddings are vectors that captures the semantic and contextual information of words. The word embedding used for this work are: FastText: The fastText algorithm created by Facebook BIBREF10 assumes every word to be n-grams of character. It helps to give the vector representations for out of vocabuary words. For the current work, fasttext based word embedding is used for generating token vectors of dimension 300 BIBREF11. Each vector corresponding to the tweet is generated by taking the average of token vectors. Universal Sentence Encoder: Developed by Google, Universal sentence encoder BIBREF12, BIBREF13 provides embeddings at sentence level. The dimension of the embedding vector is 512, irrespective of the number of tokens in the input tweet. These vectors can capture good semantic information from the sentences. For each tweet, this model generates a 512 length embedding vector and is used as features for further classification. DMD and HODMD: DMD is a method initially used in fluid dynamics which captures spatio-temporal features BIBREF14. It has been used in background-foreground separation BIBREF15, load forecasting BIBREF16, saliency detection BIBREF17 etc. For natural language processing, DMD has been first applied for sentiment analysis BIBREF18, BIBREF19. This motivated to explore DMD based feature for the present work. Dynamic mode decomposition (DMD) is a much more powerful concept and it assumes the evolution of the function over the rectangular field is effected by the mapping of a constant matrix $A$. $A$ captures the system’s inherent dynamics and the aim of the DMD is to understand using its dominant eignevalues and eigenvectors. Assumption is that this matrix $A$ is of low rank and hence the sequence of vectors $ \mathop {{x_1}}\limits _|^| ,\mathop {{x_2}}\limits _|^| ,\mathop {{x_3}}\limits _|^| ,...\mathop {,{x_k}}\limits _|^| ,...,\mathop {{x_{m + 1}}}\limits _|^| $ finally become a linearly dependent set. That is, vector $ \mathop {{x_{m + 1}}}\limits _|^|$ become linearly dependent on previous vectors. The data matrix X in terms of eigen vectors associated with matrix $A$. where, ${\Phi ^\dag }$ is pseudo inverse of ${\Phi }$. $A$ is of rank m and ${\Phi }$ have m columns. Hence, pseudo-inverse will do the job than inverse operation. The columns of ${\Phi }$ are called DMD modes and this forms the features. Time-lagged matrices are prepared as snapshot for this approach. In Eigensent BIBREF20, the authors proposed HODMD to find embedings for sentences. The authors suggested, sentences can be represented as a signal using word embeddings by taking the average of word vectors. This is intuitive because word embeddings almost obeys the laws of linear algebra, by capturing word analogies and relationships. Therefore, by considering every sentence as a multi-dimensional signal, we can capture the important transitional dynamics of sentences. Also, for the signal representation of sentences, each word vector will act as a single point in the signal. For the present work, to generate DMD and HODMD based features, Fastext based embedding is used. Proposed Approach RKS approach proposed in BIBREF21, BIBREF22, explicitly maps data vectors to a space where linear separation is possible. It has been explored for natural language processing tasks BIBREF23, BIBREF24. The RKS method provides an approximate kernel function via explicit mapping. Here, $\phi (.)$ denotes the implicit mapping function (used to compute kernel matrix), $Z(.)$ denotes the explicit mapping function using RKS and ${\Omega _k}$ denotes random variable . Figure FIGREF15 show the block diagram of the proposed approach. Experiments and Discussions ::: Data Description OLID (Offensive Language Identification Dataset) is a collection of English tweets which are annotated using a three-level hierarchical annotation model. It was collected using Twitter API and contains 14,460 annotated tweets. The task divided the data as train, trial and test, of which train and trial was initially released as starting kit, finally test was released as Test A release. All three of these partitions were highly biased, thus making the task more challenging and real time. The train set had 13,240 tweets, out of which 8840 tweets were not offensive (NOT) and 4400 tweets were offensive (OFF). Similarly, test set had 860 tweets, which had 620 not offensive and 280 offensive tweets. TableTABREF17 show the data distribution of the entire dataset. For the current work, train and test data are taken which is 14,100 tweets in number. In Sub-task A: Offensive language identification, the goal was to discriminate offensive and not-offensive twitter posts. The target classes for each instance were a) Offensive (OFF): posts that contain any form of profanity or targeted offence. This includes threats, insults, and any form of untargeted profanity. b) Not Offensive (NOT): posts that doesn't have any profanity or offense in it. The result and discussion of the top 10 teams for the sub-task A is in section for Introduction. In that, team BIBREF3 obtained highest f1-score of 82.9% Experiments and Discussions ::: Results and Comparisons This section describes the result as three different cases. Case 1 & 2 provides baseline approach to compare with the proposed RKS approcah described in case 3. Table TABREF19 gives the results of the top 5 teams of sub-task A. Team with rank 1 achieved a maximum f1-score of 82.9%. Experiments and Discussions ::: Results and Comparisons ::: Case 1: Embeddings approach In this work, we have selected word vectors generated by Google universal encoder model, Fasttext, and DMD based features. The classification using the selected features are performed using machine learning algorithms such as Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support vector machine (SVM) linear and RBF kernels, Logistic Regression, and Random kitchen sinks. The evaluation measures used are accuracy (Acc.), precision (Prec), recall, f1-score (F1). Table TABREF21 shows the classification result obtained for classical machine learning algorithms using the Google universal sentence encoder model features. It can be observed that svm linear classifier and Logistic regression has given maximum accuracy of 82.44% and 82.56%. Table TABREF22 shows the classification results obtained using the features generated by fasttext model for classical machine learning algorithms. For the fasttext model also, svm linear and logistic regression model have given maximum accuracies of 81.16% respectively. Experiments and Discussions ::: Results and Comparisons ::: Case 2: DMD approach In order to provide a comparison, we explore DMD based features. The Table TABREF24 shows the result obtained for normal DMD and HODMD based feature. The order for HODMD for the present work is 2 & 3. The classification is performed using SVM-linear kernel with control parameter value chosen as 1000 as the suitable one. We tried for other values such as 0.1, 1, 100, 500, and 1000. Figure FIGREF25 shows the control parameter versus accuracy plot which helped to fix the parameter value. Experiments and Discussions ::: Results and Comparisons ::: Case 3: RKS approach RKS approach has been used in the articles for NLP tasks [29,30,23]. In this work, we use RKS to imporve the evaluation scores as discussed previously. The RKS approach explicitly maps the embedding vectors to a dimension where the data becomes linearly separable. In that space, regularized least-square based classification (RLSC) is performed. The implementation of the RKS is taken from BIBREF29, BIBREF30. The feature vectors from Google universal sentence encoder and fasttext are explicitly mapped using RKS and the results are tabulated in Table TABREF27 and TABREF28. The Table TABREF27 shows the classification report on the proposed RKS method taking word vectors generated by Google universal encoder model as features with dimension 512. For this work, such vector is explicitly mapped to dimensions 100, 200, 500 and 1000 using RKS. The maximum accuracy obtained is 90.58% for higher dimension 1000. Table TABREF28 shows the classification report on the proposed RKS method taking word vectors generated by Fasttext model as features. For this model also, features are mapped to dimensions 100, 200, 500 and 1000. For Fasttext model, the proposed method gave a maximum accuracy of 99.53%, which is a bench marking result when compared to the literature. This result shows the discriminating capability of the features chosen, as when mapped to higher dimensions, they become linearly separable. From Table TABREF27 and TABREF28 it can be observed that as the mapping dimension increases, the evaluation score also improves. This shows the effectiveness of the RKS approach to obtain competing score. The capability of the RKS approach cane be explored on large datasets. Conclusion Offensive language detection is an important task related to social media data analysis. The nature of the content can vary as its provided by different people. The current work uses the data provided in SemEval 2019 shared task A for Offensive language identification. A comparative study is provided by exploring the effectiveness of Google universal sentence encoder, Fasttext based embedding, Dynamic Mode Decomposition based features and RKS based explicit mapping approach. For the experiments, we used the machine learning methods such as SVM linear, Random Forest, Logistic regression, Navie Bayes and Regularized least-square based classification. The measures used for evaluation are accuracy, precision, recall, and f1-score. We observed that RKS approach improved the results. However, as a future work, the proposed approach cane be explored on large datasets.
What is the Random Kitchen Sink approach?
a778b8204a415b295f73b93623d09599f242f202
What Ġis Ġthe ĠRandom ĠKitchen ĠS ink Ġapproach ?
Random ĠKitchen ĠS ink Ġmethod Ġuses Ġa Ġkernel Ġfunction Ġto Ġmap Ġdata Ġvectors Ġto Ġa Ġspace Ġwhere Ġlinear Ġseparation Ġis Ġpossible .
<s> What Ġis Ġthe ĠRandom ĠKitchen ĠS ink Ġapproach ? </s> Introduction </s> In Ġthis Ġdigital Ġera , Ġonline Ġdiscussions Ġand Ġinteractions Ġhas Ġbecome Ġa Ġvital Ġpart Ġof Ġdaily Ġlife Ġof Ġwhich Ġa Ġhuge Ġpart Ġis Ġcovered Ġby Ġsocial Ġmedia Ġplatforms Ġlike Ġtwitter , Ġfacebook , Ġinst agram Ġetc . ĠSimilar Ġto Ġreal Ġlife Ġthere Ġexist Ġanti - social Ġelements Ġin Ġthe Ġcy bers pace , Ġwho Ġtake Ġadvantage Ġof Ġthe Ġanonymous Ġnature Ġin Ġcyber Ġworld Ġand Ġindulge Ġin Ġvulgar Ġand Ġoffensive Ġcommunications . ĠThis Ġincludes Ġbullying , Ġtrolling , Ġharassment ĠB IB REF 0 , ĠB IB REF 1 Ġand Ġhas Ġbecome Ġa Ġgrowing Ġconcern Ġfor Ġgovernments . ĠYouth Ġexperiencing Ġsuch Ġvictim ization Ġwas Ġrecorded Ġto Ġhave Ġpsychological Ġsymptoms Ġof Ġanxiety , Ġdepression , Ġloneliness ĠB IB REF 1 . ĠThus Ġit Ġis Ġimportant Ġto Ġidentify Ġand Ġremove Ġsuch Ġbehaviours Ġat Ġthe Ġearliest . ĠOne Ġsolution Ġto Ġthis Ġis Ġthe Ġautomatic Ġdetection Ġusing Ġmachine Ġlearning Ġalgorithms . </s> Detect ing Ġoffensive Ġlanguage Ġfrom Ġsocial Ġmedia Ġis Ġa Ġchallenging Ġresearch Ġproblem Ġdue Ġto Ġthe Ġdifferent Ġlevel Ġof Ġambig u ities Ġpresent Ġin Ġthe Ġnatural Ġlanguage Ġand Ġnoisy Ġnature Ġof Ġthe Ġsocial Ġmedia Ġlanguage . ĠMoreover , Ġsocial Ġmedia Ġsubscribers Ġare Ġfrom Ġlingu istically Ġdiverse Ġand Ġvarying Ġcommunities . ĠOver seeing Ġthe Ġcomplication Ġof Ġthis Ġproblem , ĠB IB REF 2 Ġorganized Ġa Ġtask Ġin ĠSem E val 2019 , ĠTask Ġ6 : ĠIdent ifying Ġand ĠC ategor izing ĠOffensive ĠLanguage Ġin ĠSocial ĠMedia . ĠThe Ġtweets Ġwere Ġcollected Ġby Ġthe Ġorganizers Ġusing ĠTwitter ĠAPI Ġand Ġhave Ġannot ated Ġthem Ġin Ġa Ġhierarchical Ġmanner Ġas Ġoffensive Ġlanguage Ġpresent Ġin Ġthe Ġtweet , Ġtype Ġof Ġthe Ġoffense Ġand Ġtarget Ġof Ġthe Ġoffense . ĠThere Ġwere Ġthree Ġsub - t asks Ġaccording Ġto Ġthe Ġhierarchy Ġof Ġannotation : Ġa ) ĠTo Ġdetect Ġif Ġa Ġpost Ġis Ġoffensive Ġ( OFF ) Ġor Ġnot Ġ( NOT ), Ġb ) ĠTo ĠIdent ify Ġthe Ġtype Ġof Ġoffense Ġin Ġthe Ġpost Ġas Ġtargeted Ġthreat Ġ( T TH ), Ġtargeted Ġinsult Ġ( T IN ), Ġunt arget ed Ġ( UN T ), Ġc ) ĠTo Ġidentify Ġif Ġoffense Ġis Ġtargeted Ġto Ġorganization Ġor Ġentity Ġ( OR G ), Ġgroup Ġof Ġpeople Ġ( GR P ), Ġindividual Ġ( IND ), Ġor Ġother Ġ( OTH ). </s> The Ġdataset Ġhad Ġthe Ġfollowing Ġchallenges : </s> Dat as et Ġwas Ġcomparatively Ġsmaller . </s> Dat as et Ġwas Ġbiased / im balanced ĠB IB REF 3 . </s> In Ġthis Ġpaper , Ġwe Ġare Ġproposing Ġa Ġcomparative Ġanalysis Ġfor Ġthe Ġsub - task ĠA Ġ: Off ensive Ġlanguage Ġidentification Ġof Ġthe ĠSem E val 2019 , ĠTask Ġ6 . ĠSub - task ĠA Ġwas Ġthe Ġmost Ġpopular Ġsub - task Ġamong Ġthe Ġthree Ġand Ġhad Ġtotal Ġ104 Ġteam Ġparticipation . ĠIn ĠTable ĠT AB REF 19 , Ġthe Ġlist Ġof Ġfirst Ġ5 Ġparticipants Ġalong Ġwith Ġsystem Ġand Ġf 1 - score Ġhas Ġbeen Ġshown . </s> Off ensive Ġlanguage Ġdetection Ġis Ġone Ġof Ġthe Ġchallenging Ġand Ġinteresting Ġtopic Ġfor Ġresearch . ĠRecent Ġpast Ġhad Ġmultiple Ġshared Ġtasks Ġand Ġresearch Ġon Ġthis Ġtopic . ĠOne Ġof Ġthe Ġinitial Ġwork Ġon Ġoffensive Ġlanguage Ġusing Ġsupervised Ġclassification Ġwas Ġdone Ġby ĠYin Ġet Ġal . ĠB IB REF 4 . ĠThey Ġhave Ġused ĠN gram , ĠTF ID F Ġand Ġcombination Ġof ĠTF ID F Ġwith ĠSent iment Ġand ĠContext ual Ġas ĠFeatures . ĠSchmidt Ġand ĠW ieg and ĠB IB REF 5 Ġgave Ġa Ġsurvey Ġon Ġautomatic Ġhate Ġspeech Ġdetection Ġusing ĠN LP . ĠThe Ġauthors Ġsurveyed Ġon Ġfeatures Ġlike ĠSimple ĠSurface ĠFeatures , ĠWord ĠGeneral ization , ĠL ingu istic ĠFeatures , ĠKnowledge - Based ĠFeatures , ĠMult im od al ĠInformation Ġetc . ĠIn Ġ2013 , Ġa Ġstudy Ġon Ġdetecting Ġcyber bull ying Ġin ĠYouTube Ġcomments Ġwas Ġdone Ġby ĠDad var Ġet Ġal . ĠB IB REF 6 . ĠThey Ġhave Ġused Ġa Ġcombination Ġof Ġcontent - based , Ġcyber bull ying - specific Ġand Ġuser - based Ġfeatures Ġand Ġshowed Ġthat Ġdetection Ġof Ġcyber bull ying Ġcan Ġbe Ġimproved Ġby Ġtaking Ġuser Ġcontext Ġinto Ġaccount . ĠShared Ġtask ĠGerm E val Ġ2018 Ġorganised Ġby ĠW ieg and Ġet Ġal ., BI B REF 7 Ġwas Ġfocused Ġon Ġoffensive Ġlanguage Ġdetection Ġon ĠGerman Ġtweets . ĠIt Ġhad Ġa Ġdataset Ġof Ġover Ġ8 , 500 Ġannot ated Ġtweets Ġand Ġwas Ġtrained Ġfor Ġbinary Ġclassification Ġof Ġoffensive Ġand Ġnon - offensive Ġtweets . ĠThey Ġobtained Ġan Ġoverall Ġmac o - F 1 Ġscore Ġof Ġ76 . 77 %. ĠAnother Ġshared Ġtask Ġon ĠAgg ression ĠIdentification Ġin ĠSocial ĠMedia Ġwas Ġorganised Ġby ĠKumar Ġet Ġal ., ĠB IB REF 8 . ĠThe Ġtask Ġprovided Ġa Ġdataset Ġwith Ġ15 , 000 Ġannot ated ĠFacebook Ġposts Ġand Ġcomments Ġin ĠHindi Ġand ĠEnglish . ĠThey Ġobtained Ġa Ġweighted ĠF - score Ġof Ġ64 % Ġfor Ġboth ĠEnglish Ġand ĠHindi . ĠThe Ġrest Ġof Ġthe Ġpaper Ġis Ġstructured Ġas Ġfollows . ĠSection Ġ2 Ġexplains Ġabout Ġthe Ġmethodology Ġwith Ġformulation . ĠSection Ġ3 Ġdiscusses Ġon Ġthe ĠProp osed Ġapproach . ĠSection Ġ4 Ġtalks Ġon Ġthe ĠExper iments Ġand Ġdiscussions Ġperformed . ĠFinally , Ġconclusion Ġis Ġgiven Ġin ĠSection Ġ5 . </s> Method ology Ġ: :: ĠData ĠPre - processing </s> Data Ġpre - processing Ġis Ġa Ġvery Ġcrucial Ġstep Ġwhich Ġneeds Ġto Ġbe Ġdone Ġbefore Ġapplying Ġany Ġmachine Ġlearning Ġtasks , Ġbecause Ġthe Ġreal Ġtime Ġdata Ġcould Ġbe Ġvery Ġnoisy Ġand Ġun struct ured . ĠFor Ġthe Ġtwo Ġmodels Ġused Ġin Ġthis Ġwork , Ġpre - processing Ġof Ġtweets Ġis Ġdone Ġseparately : </s> Pre - processing Ġfor ĠGoogle Ġmodel : </s> It Ġhas Ġbecome Ġa Ġcommon Ġculture Ġto Ġuse Ġ# tags Ġacross Ġsocial Ġmedia . ĠSo Ġwe Ġhave Ġreplaced Ġmultiple Ġ# tags Ġwith Ġa Ġsingle Ġ# tag . ĠMostly Ġ@ Ġsymbol Ġid Ġused Ġto Ġmention Ġperson Ġor Ġentities Ġin Ġa Ġtweet . ĠSo Ġwe Ġreplace Ġmultiple Ġ@ sy mb ols Ġwith Ġa Ġsingle Ġ@ - ment ion . ĠSome Ġtweets Ġmay Ġcontain Ġthe Ġlink Ġto Ġa Ġwebsite Ġor Ġsome Ġother Ġur ls . ĠSo Ġwe Ġreplace Ġall Ġof Ġthese Ġwith Ġa Ġsingle Ġkeyword ĠUR LS . </s> Pre - processing Ġfor Ġfast text Ġmodel : </s> For Ġapplying Ġfast text Ġmodel Ġto Ġget Ġword Ġvectors , Ġwe Ġfollowed Ġa Ġdifferent Ġset Ġof Ġpre - processing Ġsteps . ĠFirst , Ġall Ġthe Ġnumbers , Ġpunct uation Ġmarks , Ġur ls Ġ( http :// Ġor Ġwww .) Ġand Ġsymbols Ġ( em oji , Ġ# tags , Ġ- ment ion ) Ġwere Ġremoved Ġfrom Ġthe Ġtweet Ġas Ġit Ġdo Ġnot Ġcontain Ġinformation Ġrelated Ġto Ġsentiment . ĠAfter Ġthat , Ġtoken ization Ġand Ġlower c asing Ġwas Ġapplied Ġto Ġthe Ġtweets . ĠToken ization Ġwas Ġdone Ġusing Ġtoken izer Ġfrom ĠNL T K Ġpackage ĠB IB REF 9 . ĠFinally , Ġthe Ġstop Ġword Ġare Ġremoved . ĠThe Ġlist Ġis Ġobtained Ġfrom ĠNL T K Ġpackage . </s> Method ology Ġ: :: ĠEmb edd ings </s> Word Ġembed d ings Ġare Ġubiquitous Ġfor Ġany ĠN LP Ġproblem , Ġas Ġalgorithms Ġcannot Ġprocess Ġthe Ġplain Ġtext Ġor Ġstrings Ġin Ġits Ġraw Ġform . ĠWord Ġem edd ings Ġare Ġvectors Ġthat Ġcaptures Ġthe Ġsemantic Ġand Ġcontextual Ġinformation Ġof Ġwords . ĠThe Ġword Ġembed ding Ġused Ġfor Ġthis Ġwork Ġare : </s> Fast Text : ĠThe Ġfast Text Ġalgorithm Ġcreated Ġby ĠFacebook ĠB IB REF 10 Ġassumes Ġevery Ġword Ġto Ġbe Ġn - gram s Ġof Ġcharacter . ĠIt Ġhelps Ġto Ġgive Ġthe Ġvector Ġrepresentations Ġfor Ġout Ġof Ġvoc ab uary Ġwords . ĠFor Ġthe Ġcurrent Ġwork , Ġfast text Ġbased Ġword Ġembed ding Ġis Ġused Ġfor Ġgenerating Ġtoken Ġvectors Ġof Ġdimension Ġ300 ĠB IB REF 11 . ĠEach Ġvector Ġcorresponding Ġto Ġthe Ġtweet Ġis Ġgenerated Ġby Ġtaking Ġthe Ġaverage Ġof Ġtoken Ġvectors . </s> Universal ĠSent ence ĠEnc oder : ĠDevelop ed Ġby ĠGoogle , ĠUniversal Ġsentence Ġenc oder ĠB IB REF 12 , ĠB IB REF 13 Ġprovides Ġembed d ings Ġat Ġsentence Ġlevel . ĠThe Ġdimension Ġof Ġthe Ġembed ding Ġvector Ġis Ġ512 , Ġirrespective Ġof Ġthe Ġnumber Ġof Ġtokens Ġin Ġthe Ġinput Ġtweet . ĠThese Ġvectors Ġcan Ġcapture Ġgood Ġsemantic Ġinformation Ġfrom Ġthe Ġsentences . ĠFor Ġeach Ġtweet , Ġthis Ġmodel Ġgenerates Ġa Ġ512 Ġlength Ġembed ding Ġvector Ġand Ġis Ġused Ġas Ġfeatures Ġfor Ġfurther Ġclassification . </s> D MD Ġand ĠH OD MD : ĠD MD Ġis Ġa Ġmethod Ġinitially Ġused Ġin Ġfluid Ġdynamics Ġwhich Ġcaptures Ġspat io - tem poral Ġfeatures ĠB IB REF 14 . ĠIt Ġhas Ġbeen Ġused Ġin Ġbackground - fore ground Ġseparation ĠB IB REF 15 , Ġload Ġforecasting ĠB IB REF 16 , Ġsal iency Ġdetection ĠB IB REF 17 Ġetc . ĠFor Ġnatural Ġlanguage Ġprocessing , ĠD MD Ġhas Ġbeen Ġfirst Ġapplied Ġfor Ġsentiment Ġanalysis ĠB IB REF 18 , ĠB IB REF 19 . ĠThis Ġmotivated Ġto Ġexplore ĠD MD Ġbased Ġfeature Ġfor Ġthe Ġpresent Ġwork . </s> Dynamic Ġmode Ġdecom position Ġ( D MD ) Ġis Ġa Ġmuch Ġmore Ġpowerful Ġconcept Ġand Ġit Ġassumes Ġthe Ġevolution Ġof Ġthe Ġfunction Ġover Ġthe Ġrectangular Ġfield Ġis Ġeffected Ġby Ġthe Ġmapping Ġof Ġa Ġconstant Ġmatrix Ġ$ A $. Ġ$ A $ Ġcaptures Ġthe Ġsystem âĢ Ļ s Ġinherent Ġdynamics Ġand Ġthe Ġaim Ġof Ġthe ĠD MD Ġis Ġto Ġunderstand Ġusing Ġits Ġdominant Ġe ign eval ues Ġand Ġe igen ve ctors . ĠAss umption Ġis Ġthat Ġthis Ġmatrix Ġ$ A $ Ġis Ġof Ġlow Ġrank Ġand Ġhence Ġthe Ġsequence Ġof Ġvectors Ġ$ Ġ\ math op Ġ{{ x _ 1 }} \ limits Ġ_ | ^ | Ġ, \ math op Ġ{{ x _ 2 }} \ limits Ġ_ | ^ | Ġ, \ math op Ġ{{ x _ 3 }} \ limits Ġ_ | ^ | Ġ, ... \ math op Ġ{ , { x _ k }} \ limits Ġ_ | ^ | Ġ, ... , \ math op Ġ{{ x _{ m Ġ+ Ġ1 }} }\ limits Ġ_ | ^ | Ġ$ Ġfinally Ġbecome Ġa Ġlin early Ġdependent Ġset . ĠThat Ġis , Ġvector Ġ$ Ġ\ math op Ġ{{ x _{ m Ġ+ Ġ1 }} }\ limits Ġ_ | ^ | $ Ġbecome Ġlin early Ġdependent Ġon Ġprevious Ġvectors . ĠThe Ġdata Ġmatrix ĠX Ġin Ġterms Ġof Ġe igen Ġvectors Ġassociated Ġwith Ġmatrix Ġ$ A $. </s> where , Ġ${ \ Ph i Ġ^ \ d ag Ġ} $ Ġis Ġpseudo Ġinverse Ġof Ġ${ \ Ph i Ġ} $. Ġ$ A $ Ġis Ġof Ġrank Ġm Ġand Ġ${ \ Ph i Ġ} $ Ġhave Ġm Ġcolumns . ĠHence , Ġpseudo - in verse Ġwill Ġdo Ġthe Ġjob Ġthan Ġinverse Ġoperation . ĠThe Ġcolumns Ġof Ġ${ \ Ph i Ġ} $ Ġare Ġcalled ĠD MD Ġmodes Ġand Ġthis Ġforms Ġthe Ġfeatures . </s> Time - l agged Ġmat rices Ġare Ġprepared Ġas Ġsnapshot Ġfor Ġthis Ġapproach . ĠIn ĠE ig ens ent ĠB IB REF 20 , Ġthe Ġauthors Ġproposed ĠH OD MD Ġto Ġfind Ġembed ings Ġfor Ġsentences . ĠThe Ġauthors Ġsuggested , Ġsentences Ġcan Ġbe Ġrepresented Ġas Ġa Ġsignal Ġusing Ġword Ġembed d ings Ġby Ġtaking Ġthe Ġaverage Ġof Ġword Ġvectors . ĠThis Ġis Ġintuitive Ġbecause Ġword Ġembed d ings Ġalmost Ġobe ys Ġthe Ġlaws Ġof Ġlinear Ġalgebra , Ġby Ġcapturing Ġword Ġanalog ies Ġand Ġrelationships . ĠTherefore , Ġby Ġconsidering Ġevery Ġsentence Ġas Ġa Ġmulti - dimensional Ġsignal , Ġwe Ġcan Ġcapture Ġthe Ġimportant Ġtransitional Ġdynamics Ġof Ġsentences . ĠAlso , Ġfor Ġthe Ġsignal Ġrepresentation Ġof Ġsentences , Ġeach Ġword Ġvector Ġwill Ġact Ġas Ġa Ġsingle Ġpoint Ġin Ġthe Ġsignal . ĠFor Ġthe Ġpresent Ġwork , Ġto Ġgenerate ĠD MD Ġand ĠH OD MD Ġbased Ġfeatures , ĠFast ext Ġbased Ġembed ding Ġis Ġused . </s> Prop osed ĠApproach </s> R KS Ġapproach Ġproposed Ġin ĠB IB REF 21 , ĠB IB REF 22 , Ġexplicitly Ġmaps Ġdata Ġvectors Ġto Ġa Ġspace Ġwhere Ġlinear Ġseparation Ġis Ġpossible . ĠIt Ġhas Ġbeen Ġexplored Ġfor Ġnatural Ġlanguage Ġprocessing Ġtasks ĠB IB REF 23 , ĠB IB REF 24 . ĠThe ĠR KS Ġmethod Ġprovides Ġan Ġapproximate Ġkernel Ġfunction Ġvia Ġexplicit Ġmapping . </s> Here , Ġ$\ phi Ġ( .) $ Ġdenotes Ġthe Ġimplicit Ġmapping Ġfunction Ġ( used Ġto Ġcompute Ġkernel Ġmatrix ), Ġ$ Z ( .) $ Ġdenotes Ġthe Ġexplicit Ġmapping Ġfunction Ġusing ĠR KS Ġand Ġ${ \ O mega Ġ_ k } $ Ġdenotes Ġrandom Ġvariable Ġ. </s> Figure ĠFIG REF 15 Ġshow Ġthe Ġblock Ġdiagram Ġof Ġthe Ġproposed Ġapproach . </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠData ĠDescription </s> OL ID Ġ( Off ensive ĠLanguage ĠIdentification ĠDat as et ) Ġis Ġa Ġcollection Ġof ĠEnglish Ġtweets Ġwhich Ġare Ġannot ated Ġusing Ġa Ġthree - level Ġhierarchical Ġannotation Ġmodel . ĠIt Ġwas Ġcollected Ġusing ĠTwitter ĠAPI Ġand Ġcontains Ġ14 , 460 Ġannot ated Ġtweets . ĠThe Ġtask Ġdivided Ġthe Ġdata Ġas Ġtrain , Ġtrial Ġand Ġtest , Ġof Ġwhich Ġtrain Ġand Ġtrial Ġwas Ġinitially Ġreleased Ġas Ġstarting Ġkit , Ġfinally Ġtest Ġwas Ġreleased Ġas ĠTest ĠA Ġrelease . ĠAll Ġthree Ġof Ġthese Ġpartitions Ġwere Ġhighly Ġbiased , Ġthus Ġmaking Ġthe Ġtask Ġmore Ġchallenging Ġand Ġreal Ġtime . ĠThe Ġtrain Ġset Ġhad Ġ13 , 240 Ġtweets , Ġout Ġof Ġwhich Ġ88 40 Ġtweets Ġwere Ġnot Ġoffensive Ġ( NOT ) Ġand Ġ44 00 Ġtweets Ġwere Ġoffensive Ġ( OFF ). ĠSimilarly , Ġtest Ġset Ġhad Ġ8 60 Ġtweets , Ġwhich Ġhad Ġ620 Ġnot Ġoffensive Ġand Ġ280 Ġoffensive Ġtweets . ĠTable TA B REF 17 Ġshow Ġthe Ġdata Ġdistribution Ġof Ġthe Ġentire Ġdataset . ĠFor Ġthe Ġcurrent Ġwork , Ġtrain Ġand Ġtest Ġdata Ġare Ġtaken Ġwhich Ġis Ġ14 , 100 Ġtweets Ġin Ġnumber . </s> In ĠSub - task ĠA : ĠOffensive Ġlanguage Ġidentification , Ġthe Ġgoal Ġwas Ġto Ġdiscriminate Ġoffensive Ġand Ġnot - offensive Ġtwitter Ġposts . ĠThe Ġtarget Ġclasses Ġfor Ġeach Ġinstance Ġwere Ġa ) ĠOffensive Ġ( OFF ): Ġposts Ġthat Ġcontain Ġany Ġform Ġof Ġprof anity Ġor Ġtargeted Ġoffence . ĠThis Ġincludes Ġthreats , Ġinsults , Ġand Ġany Ġform Ġof Ġunt arget ed Ġprof anity . Ġb ) ĠNot ĠOffensive Ġ( NOT ): Ġposts Ġthat Ġdoesn 't Ġhave Ġany Ġprof anity Ġor Ġoffense Ġin Ġit . ĠThe Ġresult Ġand Ġdiscussion Ġof Ġthe Ġtop Ġ10 Ġteams Ġfor Ġthe Ġsub - task ĠA Ġis Ġin Ġsection Ġfor ĠIntroduction . ĠIn Ġthat , Ġteam ĠB IB REF 3 Ġobtained Ġhighest Ġf 1 - score Ġof Ġ82 . 9 % </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠResults Ġand ĠCompar isons </s> This Ġsection Ġdescribes Ġthe Ġresult Ġas Ġthree Ġdifferent Ġcases . ĠCase Ġ1 Ġ& Ġ2 Ġprovides Ġbaseline Ġapproach Ġto Ġcompare Ġwith Ġthe Ġproposed ĠR KS Ġappro c ah Ġdescribed Ġin Ġcase Ġ3 . ĠTable ĠT AB REF 19 Ġgives Ġthe Ġresults Ġof Ġthe Ġtop Ġ5 Ġteams Ġof Ġsub - task ĠA . ĠTeam Ġwith Ġrank Ġ1 Ġachieved Ġa Ġmaximum Ġf 1 - score Ġof Ġ82 . 9 %. </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠResults Ġand ĠCompar isons Ġ: :: ĠCase Ġ1 : ĠEmb edd ings Ġapproach </s> In Ġthis Ġwork , Ġwe Ġhave Ġselected Ġword Ġvectors Ġgenerated Ġby ĠGoogle Ġuniversal Ġenc oder Ġmodel , ĠFast text , Ġand ĠD MD Ġbased Ġfeatures . ĠThe Ġclassification Ġusing Ġthe Ġselected Ġfeatures Ġare Ġperformed Ġusing Ġmachine Ġlearning Ġalgorithms Ġsuch Ġas ĠRandom ĠForest Ġ( RF ), ĠDecision ĠTree Ġ( DT ), ĠNa ive ĠBay es Ġ( NB ), ĠSupport Ġvector Ġmachine Ġ( S VM ) Ġlinear Ġand ĠRB F Ġkernels , ĠLog istic ĠReg ression , Ġand ĠRandom Ġkitchen Ġsinks . ĠThe Ġevaluation Ġmeasures Ġused Ġare Ġaccuracy Ġ( Acc .), Ġprecision Ġ( Pre c ), Ġrecall , Ġf 1 - score Ġ( F 1 ). ĠTable ĠT AB REF 21 Ġshows Ġthe Ġclassification Ġresult Ġobtained Ġfor Ġclassical Ġmachine Ġlearning Ġalgorithms Ġusing Ġthe ĠGoogle Ġuniversal Ġsentence Ġenc oder Ġmodel Ġfeatures . ĠIt Ġcan Ġbe Ġobserved Ġthat Ġs vm Ġlinear Ġclass ifier Ġand ĠLog istic Ġregression Ġhas Ġgiven Ġmaximum Ġaccuracy Ġof Ġ82 . 44 % Ġand Ġ82 . 56 %. </s> Table ĠT AB REF 22 Ġshows Ġthe Ġclassification Ġresults Ġobtained Ġusing Ġthe Ġfeatures Ġgenerated Ġby Ġfast text Ġmodel Ġfor Ġclassical Ġmachine Ġlearning Ġalgorithms . ĠFor Ġthe Ġfast text Ġmodel Ġalso , Ġs vm Ġlinear Ġand Ġlog istic Ġregression Ġmodel Ġhave Ġgiven Ġmaximum Ġaccur acies Ġof Ġ81 . 16 % Ġrespectively . </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠResults Ġand ĠCompar isons Ġ: :: ĠCase Ġ2 : ĠD MD Ġapproach </s> In Ġorder Ġto Ġprovide Ġa Ġcomparison , Ġwe Ġexplore ĠD MD Ġbased Ġfeatures . ĠThe ĠTable ĠT AB REF 24 Ġshows Ġthe Ġresult Ġobtained Ġfor Ġnormal ĠD MD Ġand ĠH OD MD Ġbased Ġfeature . ĠThe Ġorder Ġfor ĠH OD MD Ġfor Ġthe Ġpresent Ġwork Ġis Ġ2 Ġ& Ġ3 . ĠThe Ġclassification Ġis Ġperformed Ġusing ĠS VM - linear Ġkernel Ġwith Ġcontrol Ġparameter Ġvalue Ġchosen Ġas Ġ1000 Ġas Ġthe Ġsuitable Ġone . ĠWe Ġtried Ġfor Ġother Ġvalues Ġsuch Ġas Ġ0 . 1 , Ġ1 , Ġ100 , Ġ500 , Ġand Ġ1000 . ĠFigure ĠFIG REF 25 Ġshows Ġthe Ġcontrol Ġparameter Ġversus Ġaccuracy Ġplot Ġwhich Ġhelped Ġto Ġfix Ġthe Ġparameter Ġvalue . </s> Exper iments Ġand ĠDisc ussions Ġ: :: ĠResults Ġand ĠCompar isons Ġ: :: ĠCase Ġ3 : ĠR KS Ġapproach </s> R KS Ġapproach Ġhas Ġbeen Ġused Ġin Ġthe Ġarticles Ġfor ĠN LP Ġtasks Ġ[ 29 , 30 , 23 ]. ĠIn Ġthis Ġwork , Ġwe Ġuse ĠR KS Ġto Ġimp or ve Ġthe Ġevaluation Ġscores Ġas Ġdiscussed Ġpreviously . ĠThe ĠR KS Ġapproach Ġexplicitly Ġmaps Ġthe Ġembed ding Ġvectors Ġto Ġa Ġdimension Ġwhere Ġthe Ġdata Ġbecomes Ġlin early Ġsepar able . ĠIn Ġthat Ġspace , Ġregular ized Ġleast - square Ġbased Ġclassification Ġ( RL SC ) Ġis Ġperformed . ĠThe Ġimplementation Ġof Ġthe ĠR KS Ġis Ġtaken Ġfrom ĠB IB REF 29 , ĠB IB REF 30 . ĠThe Ġfeature Ġvectors Ġfrom ĠGoogle Ġuniversal Ġsentence Ġenc oder Ġand Ġfast text Ġare Ġexplicitly Ġmapped Ġusing ĠR KS Ġand Ġthe Ġresults Ġare Ġtab ulated Ġin ĠTable ĠT AB REF 27 Ġand ĠT AB REF 28 . </s> The ĠTable ĠT AB REF 27 Ġshows Ġthe Ġclassification Ġreport Ġon Ġthe Ġproposed ĠR KS Ġmethod Ġtaking Ġword Ġvectors Ġgenerated Ġby ĠGoogle Ġuniversal Ġenc oder Ġmodel Ġas Ġfeatures Ġwith Ġdimension Ġ512 . ĠFor Ġthis Ġwork , Ġsuch Ġvector Ġis Ġexplicitly Ġmapped Ġto Ġdimensions Ġ100 , Ġ200 , Ġ500 Ġand Ġ1000 Ġusing ĠR KS . ĠThe Ġmaximum Ġaccuracy Ġobtained Ġis Ġ90 . 58 % Ġfor Ġhigher Ġdimension Ġ1000 . </s> Table ĠT AB REF 28 Ġshows Ġthe Ġclassification Ġreport Ġon Ġthe Ġproposed ĠR KS Ġmethod Ġtaking Ġword Ġvectors Ġgenerated Ġby ĠFast text Ġmodel Ġas Ġfeatures . ĠFor Ġthis Ġmodel Ġalso , Ġfeatures Ġare Ġmapped Ġto Ġdimensions Ġ100 , Ġ200 , Ġ500 Ġand Ġ1000 . ĠFor ĠFast text Ġmodel , Ġthe Ġproposed Ġmethod Ġgave Ġa Ġmaximum Ġaccuracy Ġof Ġ99 . 53 %, Ġwhich Ġis Ġa Ġbench Ġmarking Ġresult Ġwhen Ġcompared Ġto Ġthe Ġliterature . ĠThis Ġresult Ġshows Ġthe Ġdiscriminating Ġcapability Ġof Ġthe Ġfeatures Ġchosen , Ġas Ġwhen Ġmapped Ġto Ġhigher Ġdimensions , Ġthey Ġbecome Ġlin early Ġsepar able . ĠFrom ĠTable ĠT AB REF 27 Ġand ĠT AB REF 28 Ġit Ġcan Ġbe Ġobserved Ġthat Ġas Ġthe Ġmapping Ġdimension Ġincreases , Ġthe Ġevaluation Ġscore Ġalso Ġimproves . ĠThis Ġshows Ġthe Ġeffectiveness Ġof Ġthe ĠR KS Ġapproach Ġto Ġobtain Ġcompeting Ġscore . ĠThe Ġcapability Ġof Ġthe ĠR KS Ġapproach Ġcane Ġbe Ġexplored Ġon Ġlarge Ġdatasets . </s> Conclusion </s> Off ensive Ġlanguage Ġdetection Ġis Ġan Ġimportant Ġtask Ġrelated Ġto Ġsocial Ġmedia Ġdata Ġanalysis . ĠThe Ġnature Ġof Ġthe Ġcontent Ġcan Ġvary Ġas Ġits Ġprovided Ġby Ġdifferent Ġpeople . ĠThe Ġcurrent Ġwork Ġuses Ġthe Ġdata Ġprovided Ġin ĠSem E val Ġ2019 Ġshared Ġtask ĠA Ġfor ĠOffensive Ġlanguage Ġidentification . ĠA Ġcomparative Ġstudy Ġis Ġprovided Ġby Ġexploring Ġthe Ġeffectiveness Ġof ĠGoogle Ġuniversal Ġsentence Ġenc oder , ĠFast text Ġbased Ġembed ding , ĠDynamic ĠMode ĠDec om position Ġbased Ġfeatures Ġand ĠR KS Ġbased Ġexplicit Ġmapping Ġapproach . ĠFor Ġthe Ġexperiments , Ġwe Ġused Ġthe Ġmachine Ġlearning Ġmethods Ġsuch Ġas ĠS VM Ġlinear , ĠRandom ĠForest , ĠLog istic Ġregression , ĠNav ie ĠBay es Ġand ĠRegular ized Ġleast - square Ġbased Ġclassification . ĠThe Ġmeasures Ġused Ġfor Ġevaluation Ġare Ġaccuracy , Ġprecision , Ġrecall , Ġand Ġf 1 - score . ĠWe Ġobserved Ġthat ĠR KS Ġapproach Ġimproved Ġthe Ġresults . ĠHowever , Ġas Ġa Ġfuture Ġwork , Ġthe Ġproposed Ġapproach Ġcane Ġbe Ġexplored Ġon Ġlarge Ġdatasets .