DataMiningEmotionSNSpreprint.docx

DataMiningEmotionSNSpreprint.docx

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DataMiningEmotionSNSpreprint

DataMiningEmotioninSocialNetworkCommunication:

GenderdifferencesinMySpace

MikeThelwall,DavidWilkinson,SukhvinderUppal

StatisticalCybermetricsResearchGroup,SchoolofComputingandInformationTechnology,UniversityofWolverhampton,WulfrunaStreet,WolverhamptonWV11SB,UK.

E-mail:

m.thelwall@wlv.ac.uk,d.wilkinson@wlv.ac.uk,s

Tel:

+441902321470Fax:

+441902321478

Despitetherapidgrowthinsocialnetworksitesandindataminingforemotion（sentimentanalysis）,littleresearchhastiedthetwotogetherandnonehashadsocialsciencegoals.ThisarticleexaminestheextenttowhichemotionispresentinMySpacecomments,usingacombinationofdataminingandcontentanalysis,andexploringageandgender.Arandomsampleof819publiccommentstoorfromU.S.userswasmanuallyclassifiedforstrengthofpositiveandnegativeemotion.Twothirdsofthecommentsexpressedpositiveemotionbutaminority（20%）containednegativeemotion,confirmingthatMySpaceisanextraordinarilyemotion-richenvironment.Femalesarelikelytogiveandreceivemorepositivecommentsthanmales,butthereisnodifferencefornegativecomments.Itisthuspossiblethatfemalesaremoresuccessfulsocialnetworksiteuserspartlybecauseoftheirgreaterabilitytotextuallyharnesspositiveaffect.

Introduction

Thecomputer-aideddetection,analysisandapplicationofemotion,particularlyintext,hasbeenagrowthareainrecentyears（Pang&Lee,2008）.Almostallofthisresearchhasfocusedondetectingopinionsinlargebodiesoftext.Forexample,aprogrammightscanalargenumberofcustomercommentsorreviewsofamanufacturer’sproductsandreportwhichaspectsofwhichproductstendedtoreceivepositiveandnegativefeedback.Knownasopinionmining（computerscience）orsentimentanalysis（computationallinguistics）,thisapproachtypicallyworksbyidentifyingpositivewordsorphrasesinfreetext（e.g.,“Ilike”,or“rocked!

”）andtyingthemtotheobjectsreferredto（e.g.,“theleatherseats”,“thepackageofextras”）.Fromawidersocialperspective,emotionisimportanttohumancommunicationandlifeandsoitseemsthatthetimeisripetoexploitadvancesandintuitionsfromopinionmininginordertodetectemotioninawidervarietyofcontextsandforprimarilysocialratherthancommercialgoals.Inparticular,isitnowpossibletodetectemotioninpeople’stextualcommunicationsandusethistogaindeeperinsightsintoissuesforwhichemotioncanplayarole?

Forinstance,howimportantisemotionalexpressionfor:

effectivecommunicationbetweenfriendsoracquaintances,winninganonlineargument,automaticallydetectingabusivecommunicationpatternsinchatrooms,ordetectingpredatorybehaviouronline?

ThisarticlebeginstheprocessofmovingfromopinionminingtoemotiondetectionbyusingacasestudyofMySpacecommentstodemonstratethatitispossibletoextractemotion-bearingcommentsonalargescale,togainpreliminaryresultsaboutthesocialroleofemotionandtoidentifykeyproblemsforthetaskofidentifyingemotionininformaltextualcommunicationsonline.Hence,althoughitispreliminaryandexploratoryitisdesignedtoreportusefulinformationforfutureemotiondetectionresearchandforthoseinterestedinsocialnetworkcommunication.Largescaledatacollectionandanalysisfromsocialnetworksiteshasalreadybeenusedforsocialscienceresearchgoals（Kleinberg,2008）butnotyetincombinationwithemotiondetection.

Background

Thissectionreviewsseveralaspectsofthebackgroundtoautomaticemotiondetectioninsocialnetworksites:

opinionmining（i.e.automaticopiniondetection）;thepsychologyandsociologyofemotion（becauseemotionisacomplexconstruct）;andsocialnetworkcommunicationandusage.Genderdifferencesinemotionandlanguagearealsodiscussed.

OpinionMiningandTextMining

Opinionminingorsentimentanalysisistheautomaticdetectionofopinionsfromfreetext.Thisresearchareahasbeenpartlymotivatedbythecommercialgoalofgivingcheap,detailedandtimelycustomerfeedbacktobusinesses（Pang&Lee,2008）.BeforetheInternet,businesseswouldhavetorelyuponrelativelyslowandexpensivemethodsofgainingcustomerfeedback,suchasphoneormailsurveys,interviewsandfocusgroups.Online,however,theymaybeabletogainfeedbackfromonlinecustomerreviews,blogs,commentsandchatroomdiscussion,assumingthatacomputerprogramcanfilterouttherelevantdatafromtherestoftheweboraparticularreviewswebsite.Inthiscontext,thegoalofopinionminingistoidentifypositiveandnegativeopinionsinfreetextandtoassociatethisopinionwithrelevantobjects.Thegoalmightbedetailinthesenseofidentifyingwhatisdiscussedandhow（e.g.,whichaspectsofacararelikedordisliked）,orthegoalmightbeajudgementinthesenseofdiagnosingthenatureandstrengthofopinion（e.g.,diagnosinghowmuchareviewerlikedafilmfromtheironlinereview）.

Opinionminingisoftensplitintotwoconsecutivetasks:

detectingwhichtextsegments（e.g.,sentences）containopinionsandthepolarityandperhapsstrengthofthatopinion（Pang&Lee,2008）.Asimpletechniquecountshowoftenpositiveandnegativewordsoccurorhowoftentheyco-occurinsentenceswithgiventargetterms（e.g.,“enginereliability”）.Whilstfullmachinecomprehensionoftextiscurrentlyimpossible,computationallinguisticstechniquescanpartlyanalysethestructureoftext,usingittomoreaccuratelydetectsentiment.Thisapproachmightincorporatenegatingwords（Das&Chen,2001）like“not”,boosterwordslike“very”andgrammaticalstructurescommoninsentiment-bearingsentences（Turney,2002）.ItreliesuponreasonablygrammaticallycorrectEnglishtofunctioneffectively,however,whichmakesitlessusefulinenvironmentslikesocialnetworksiteswithmuchinformallanguage.Manyrefinementsoftheaboveapproacheshavebeenproposed（e.g.,Konig&Brill,2006;Turney,2002）.

Textminingapplicationshavealsobeendevelopedinpsychology,communicationstudies,managementandcorpuslinguistics（forareviewsee:

Pennebaker,Mehl,&Niederhoffer,2003）.Forinstance,somepsychologicaldisorderscanbequitereliablydiagnosedinpatientsbaseduponasimplewordfrequencyanalysisofspeech（Oxman,Rosenberg,&Tucker,1982）;politicalstatements（Hart,2001）andbusinessmissionstatements（Short&Palmer,2008）havebeenanalysedforthestrengthofvariablesincludingoptimism;andafactoranalysisacrossawiderangeoftextgenreshasidentifiedthatthedegreeofauthorinvolvementinatextasopposedtoaninformationalorientation（arguablyaweakexpressionofemotion）issomethingthattendstobeconstantwithingenresbutvariesbetweengenres（Biber,2003）.

Thepsychologyofemotion

Manydifferentaspectsofemotioncanbemeasured,including:

individuals’self-reportsoffeelings,neurologicalchanges,autonomicsystemreactions,andbodilyactions–includingfacialmovements（Mauss&Robinson,2009）.Theseseemtooverlaptobetweendifferentemotionsleadingthemtobedescribedassyndromesratherthanclearsetsofidentifiablefeatures.Eckman（1992）andothersneverthelessarguethattherearebasicorfundamentalemotionsthatarerelativelyuniversallyrecognisedandapparentlyexperiencedbyhumans,andthattheseexistasaresultofevolutionarypressure.Forexample,autonomicchangesandcognitiveprocessesduringfearprepareapersontorunawayfromdanger.Insupportofthis,thereisscientificevidencethatatleastfivedifferentemotions（fear,disgust,anger,happiness,sadness）aredemonstrablydifferentinthesenseofactivatingdifferentcombinationsofbrainregions（Murphy,Nimmo-Smith,&Lawrence,2003）;addingsurprisegivesEkman’s（1992）mainlistofsixbasicemotions.Eckman’s（1992）evidencefoundinsupportofemotionsbeingbasicisasetofsixgeneralcharacteristicscommontoallbasicemotions（e.g.,briefduration,presenceinotherprimates）andthreetypesofcharacteristicthatexistbutdifferbetweenemotions:

signals（e.g.,facialexpressions）;physiology（e.g.,autonomicnervoussystemactivitypatterns）;andantecedentevents（e.g.,adangerouseventoccurring）.

Theabovelistexcludessomeemotionsconsideredimportantbyothers,suchasanxiety,guilt,shame,envy,jealousy,compassionandlove（Lazarus,1991,p.122）.Non-basicemotionsaresometimesseenascombinationsofbasicemotionsandseemtovarymorebetweencultures.Emotionperceptionisculture-specificbecausesomesocietiesdescribeemotionsneverapparentlyexperiencedelsewhere（e.g.,theoft-mentioned“stateofbeingawildpig”（Newman,1964）inaNewGuineacommunity）.

Fromtheperspectiveoffelthumanexperiencesratherthanattheneurologicalordescriptivelevels,itseemsthattherearetwofundamentaldimensionsratherthanarangeofdifferingkindsofemotions（Fox,2008,p.120）.First,thevalenceofanexperiencedemotionisthedegreetowhichitisstronglypositiveornegative.Second,thelevelofarousalfeltistheamountofenergyperceived（e.g.,fromlethargictohyperactive）.Thisassertionapparentlycontradictstheneurologicalevidenceaboveofatleastfiveemotionsandthelinguisticevidenceintheformoftheexistenceofawiderangeofnon-synonymoustermsforemotions.Nevertheless,researchhasshownthatpeopledescribingthesametraumaticeventmayuseawiderangeofdifferentemotionalterms（e.g.,sad,angry,upset）almostindiscriminately（Barrett,2006）andthatthetwodimensionsofvalenceandarousalseemtobethekeyunderlyingfactors.Aconsequenceofthisisthatidentifyingvalenceandarousalislikelytobefareasierandmorer