a fine-grained analysis of user-generated content to support decision making
Post on 18-Dec-2014
459 Views
Preview:
DESCRIPTION
TRANSCRIPT
AFine‐GrainedAnalysisofUser‐GeneratedContenttoSupportDecisionMaking
MarcirioSilveiraChavesh/p://mchaves.wikidot.com
Informa<onSystemsResearchGroup
BusinessandInforma<onTechnologyResearchCentre(BITREC)Ins<tuteforScien<ficandTechnologicalResearchofUniversidadeAtlân<ca(ISTR)
Workshop
User‐GeneratedContent(UGC)• Asknownas
– User‐GeneratedData– User‐CreatedContent– User‐ContributedData– Consumer‐GeneratedMedia
– …
• Canbeexpressedthrought– Opinions– Reviews– Comments– Posts
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 2
• Notes:• Alltheexamplesdescribedinthisworkshoparerealdata.• Somepapersmen<onedhereareunderreview.• Colorlegend:
• Examples• Posi<vefeature• Nega<vefeature
ExampleofUGC• AnopinionpostedinFacebookDec‐10‐2011,12:30pm
– “wouldhighlyrecommendInfinityMotorcycles,Southamptonforallmotorbikinggear.Veryreasonablepeople.Earliertheygavemeafullmoneybackforaunused(a\erexplainingwhyitwasunused)ladiesmotorbikejacket(nodefectswhasoever)andtodaythezipperonmynewjacketwasbrokenandtheygavemeabrandnewone(noques<onsasked,noreceiptbusinessandnofusscreated).FiveStarservice.”
– Thisuserhad226friends.
Apr‐18‐12 3MarcirioChaves‐marcirioc@uatlan<ca.pt
Somesta<s<csaboutUGC• Morethan50%ofallinternetvisitsarenowtoUGC/socialmediasites.
• Morethan75%of<mespentontheinternetis"social”.
• Facebooknowcapturesasmuch<mespentontheinternetasGoogle,Yahoo,andAOL.
• Morethan80%ofconsumersareinfluencedbySocialMarkeJng.
Source: http://www.bbrisco.com/2010/05/social.html
Apr‐18‐12 4MarcirioChaves‐marcirioc@uatlan<ca.pt
MainObjec<vesofthisWorkshop• In‐depthanalysisofUGC
• UseUGCtosupportdecisionmaking
• StudyadomainontologytosupportAr<ficialIntelligencetasks
• Addressapproachesforsen<mentanalysis
• Fromtheorytoprac<ce:Hands‐onSessionApr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 5
OutlinePart1
• WorkshopContext
• User‐GeneratedContent(UGC)
• Characterisa<onofUGC• KnowledgeEngineering‐
OntologyDevelopment
• Hands‐onSession(IndividualTask):DealingwithUGC
Part2
• Sen<mentAnalysis/OpinionMining
• PolarityRecognizerinPortuguese(PIRPO)
• Informa<onVisualisa<on
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 6
ContextWorkshop
AframeworkforCustomerKnowledgeManagementbasedonSocialSeman<cWeb.
Chaves,MarcirioSilveira;Trojahn,CássiaandPedron,Cris<aneDrebes.AFrameworkforCustomerKnowledgeManagementbasedonSocialSeman<cWeb:AHotelSectorApproach.In:CustomerRela<onshipManagementandtheSocialandSeman<cWeb:EnablingCliensConexus.Colomo‐Palacios,R.;Varajão,J.andSoto‐Acosta,P.(Eds.).p.141‐157,Hershey,PA:IGIGlobal,2012.ISBN:978‐161‐35‐0044‐6
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 7
AnFine‐grainedAnalysisofUGC• OverallopinionaboutatopicisonlyapartoftheinformaJonofinterest.
• Document‐levelsenJmentclassificaJonfailstodetectsen<mentaboutindividualaspectsofthetopic.Inreality,forexample,thoughonecouldbegenerallyhappyabouthiscar,hemightbedissaJsfiedbytheenginenoise.
• Tothemanufacturers,theseindividualweaknessesandstrengthsareequallyimportanttoknow,orevenmorevaluablethantheoverallsa<sfac<onlevelofcustomers.(Tangetal.2009)
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 8
UGC
AnopinionissimplyaposiJveornegaJvesenJment,view,aPtude,emoJon,or
appraisalaboutanenJtyoranaspectoftheenJty(HuandLiu,2004;Liu,2006)fromanopinionholder(Bethardetal.,2004;Kimand
Hovy,2004;Wiebeetal.,2005).
Apr‐18‐12 9MarcirioChaves‐marcirioc@uatlan<ca.pt
Characterisa<onofUGC• Opinion’sCharacterisa<on– Iuseandextendthedefini<onproposedby(Dingetal.,2008;Liu,2010;Mar<nandWhite,2005)toanalysethesentencesofreviews.
– Letthereviewber.
– Inthemostgeneralcase,rischaracterisedasasetofthefollowingelements{O,F,SO,H,S,A,R,I,SG},where:
Apr‐18‐12 10MarcirioChaves‐marcirioc@uatlan<ca.pt
Characterisa<onofUGC• Opinion’sCharacterisa<on– O:Object– F:Feature– SO:Seman<c‐Orienta<on– H:Holder– S:Source– A:A%tude– SG:Sugges.on– R:Recommenda.on– I:Inten.on
Apr‐18‐12 11MarcirioChaves‐marcirioc@uatlan<ca.pt
Characterisa<onofUGC1 ‐Object(O)– Anobjectisaproduct(e.g.movieandbook)oraservice(e.g.hotelandrestaurant)underreviewwhichiscomposedbyfeatures.– ObjectsarealsocalledenJJes.
2‐Feature(F)– Afeatureisacomponentorpartofanobject.• actorandphotographyarefeaturesonamovie.• poolandstaffarefeaturesonahotel.
– FeaturesarealsocalledaXributesorfacets.– Afeaturecanbemen<onedexplicitlyorimplicitlyinareview(Dingetal.2008).
Apr‐18‐12 12MarcirioChaves‐marcirioc@uatlan<ca.pt
Characterisa<onofUGC2.1‐ExplicitFeature(F)– Ifafeaturefappearsinreviewr,itiscalledanexplicitfeatureinr.
– Thehotelislocatedverynearthecentercity.• loca<onisanexplicitfeature.
2.2‐ImplicitFeature(F):– Iffdoesnotappearinrbutisimplied,itiscalledanimplicitfeatureinr.
– Hotelisfarfrompublictransporta<on.• loca<onisanimplicitfeature.
Apr‐18‐12 13MarcirioChaves‐marcirioc@uatlan<ca.pt
Characterisa<onofUGC3‐Sentence‐OrientaJon(SO)– Areviewconsistsofasequenceofsentencesr=⟨s1,s2,…,sm⟩(Dingetal.,2008).
– Asentencecanbeevaluatedasthefollowingperspec<ves:
Apr‐18‐12 14MarcirioChaves‐marcirioc@uatlan<ca.pt
Characterisa<onofUGC3.1ObjecJvity– Anobjec<vesentencecontainsormenJonfacts.• Thishotelisfarfromtheairport,ca.15km.
– Asubjec<vesentencedoesnotmenJonanyfact.• Theparkingcouldbefree.
3.2Polarity– ItdescribestheorientaJonpresentinasentence(i.e.posiJve,negaJve,neutralandirrelevant).
Apr‐18‐12 15MarcirioChaves‐marcirioc@uatlan<ca.pt
Characterisa<onofUGC3.3Intensity(strengthofthepolarity)– Itreferstothestrengthoftheprivatestatethatisbeingexpressed,inotherwords,howstrongisanemo<onoraconvic<onofbelief(Wilson,2008).
– Itdescribeshowintenseitwastheexperienceusingaproductorservice:• veryposiJve,posiJve,neutral,negaJveandverynegaJve.
• Verykindlystaff.referstoaveryposi<veimpressiononthestaffservice.
Apr‐18‐12 16MarcirioChaves‐marcirioc@uatlan<ca.pt
Characterisa<onofUGC4‐OpinionHolder(H)– Theholderofapar<cularopinionisthepersonortheorganisaJonthatholdstheopinion(Dingetal.,2008).
– Aholderisiden<fiedwithdemographiccharacterisJcs(e.g.name,cityandcountry).
– Sitessuchastripadvisor.comandbooking.comclassifyholdersastypesincluding:• familieswitholderchildren
• familieswithyoungchildren• maturecouples
• groupsoffriends• solotravellers• youngcouples
Apr‐18‐12 17MarcirioChaves‐marcirioc@uatlan<ca.pt
Characterisa<onofUGC5–Source– Aninforma<onsourceisawebsitewhichprovidesasetofreviews.• tripadvisor.com
• booking.com• amazon.com
• A:A%tude
• SG:Sugges.on• R:Recommenda.on
• I:Inten.onApr‐18‐12 18MarcirioChaves‐marcirioc@uatlan<ca.pt
OutlinePart1
• WorkshopContext
• User‐GeneratedContent(UGC)
• Characterisa<onofUGC• KnowledgeEngineering‐
OntologyDevelopment
• Hands‐onSession(IndividualTask):DealingwithUGC
Part2
• Sen<mentAnalysis/OpinionMining
• PolarityRecognizerinPortuguese(PIRPO)
• Informa<onVisualisa<on
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 19
Limita<onsforrepresen<ngknowledgeintheaccommoda<onsector
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 20
language?
Morelimita<ons• Actually,webagentsareunabletoanswerques<onssuchas:– WhatarethehotelswithlongerindoorswimmingpoolJmetableinRoma?
– WhatarethehotelswiththecheapestbreakfastinLisbon?
– WhatarethecheapesthotelswithfamilysuiteroomwithseaviewinBarcelona?
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 21
KnowledgeEngineering• OntologyasasupporttoevaluateUGC– Setofconceptstoaspecificdomain
– Humanandmachinereadable– Supporttofine‐grainedanalysisoftheinstances(e.g.reviews)
– Hontology(Hstandsforhotel,hostalandhostel)• Arobust,coherentandmul<lingualrepresenta<onoftheaccommoda<onsector.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 22
ContextWorkshop
AframeworkforCustomerKnowledgeManagementbasedonSocialSeman<cWeb.
Chaves,MarcirioSilveira;Trojahn,CássiaandPedron,Cris<aneDrebes.AFrameworkforCustomerKnowledgeManagementbasedonSocialSeman<cWeb:AHotelSectorApproach.In:CustomerRela<onshipManagementandtheSocialandSeman<cWeb:EnablingCliensConexus.Colomo‐Palacios,R.;Varajão,J.andSoto‐Acosta,P.(Eds.).p.141‐157,Hershey,PA:IGIGlobal,2012.ISBN:978‐161‐35‐0044‐6
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 23
KnowledgeEngineering• DevelopmentMethodology
– Iden<fyexis<ngontologiesonrelateddomains– Selectthemainconceptsandproper<es– Organizeconceptsandproper<eshierarchicallyintocategories– Translatetheontology(manual)– Expandconceptsandproper<esbasedoncomments– Translatethenewconceptsandproper<es(manual)– Generatetheontologyinseveralformats
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 24
Chaves,M.S.andTrojahn,C.TowardsaMulJlingualOntologyforOntology‐drivenContentMininginSocialWebSites.Proc.oftheISWC2010Workshops,VolumeI,1stInternaJonalWorkshoponCross‐CulturalandCross‐LingualAspectsoftheSemanJcWeb.Shanghai,China,November7th,2010.
KnowledgeEngineering• Hontology– AmulJlingualontologyfortheaccommodaJonsector.
• DemoProtégé
Chaves,M.S.;Freitas,L.A.andVieira,R.(2012).Hontology:AmulJlingualontologyfortheaccommodaJonsector.4thInternaJonalConferenceonKnowledgeEngineeringandOntologyDevelopment,Barcelona,Spain,4‐7October.(SubmiXed)
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 25
KnowledgeEngineering
PreliminaryHontologySta<s<cs
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 26
Metrics ValueNumberofConcepts 285NumberofObjectProper<es 10NumberofDataProper<es 31
ConceptAxiomsSubconceptaxioms 270Equivalentconceptsaxioms 4Disjointconceptsaxioms 93
ObjectPropertyAxiomsFunc<onalobjectpropertyaxioms 6Objectpropertydomainaxioms 11Objectpropertyrangeaxioms 8
DataPropertyAxiomsFunc<onaldatapropertyaxioms 12Objectdatadomainaxioms 17Objectdatarangeaxioms 1
Hands‐onSession• Theaimofthishands‐onsessionistoallowyouthinking
in‐depthaboutUGConthecontextoftheaccommoda<onsector.
• Youaregoingtoreceiveasetof4or5reviewsaboutaccommoda<onsandshouldevaluateeachoneaccordingtothefollowingparameters:– Featurespresentinthereview(seetheconceptsofHontology)
– Intensity(StrengthofthePolarity):(verynega<ve,nega<ve,neutral,posi<ve,veryposi<ve)
• Notes:– Evaluateonefeatureperline.– Please,saveyoursheetinanotherfileandsendtomschaves@gmail.com.Subject:UB:GX
– X=numberofthegroup.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 27
OutlinePart1
• WorkshopContext
• User‐GeneratedContent(UGC)
• Characterisa<onofUGC• KnowledgeEngineering‐
OntologyDevelopment
• Hands‐onSession(IndividualTask):DealingwithUGC
Part2
• Sen<mentAnalysis/OpinionMining
• PolarityRecognizerinPortuguese(PIRPO)
• Informa<onVisualisa<on
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 28
Sen<mentAnalysis• AnalysisandautomaJcextracJonofSemanJcOrientaJon
• SemanJcorientaJonreferstothepolarityandstrengthofwords,phrases,ortexts.
• Approaches– Lexicon‐based
• Dic<onariesofwordsannotatedwiththeword´sseman<corienta<on,orpolarity.
• AmanuallybuiltdicJonaryprovidesasolidfoundaJonforalexicon‐basedapproach(Taboadaet.al.,2011).
– StaJsJcalorMachine‐learning• Supervisedclassifica<on
Apr‐18‐12 29MarcirioChaves‐marcirioc@uatlan<ca.pt
Sen<mentAnalysis• Lexicon‐basedApproach– Sen<ment‐bearingwords:alistofnouns,verbs,adjecJvesandadverbs(Chesleyetal.,2006)• useverbsandadjec<vestoclassifyEnglishopinionatedblogtexts.
– ListofconjuncJonsandconnecJves(Liu,2010).– Useofauxiliaryverbstogetfeaturesandopinion‐orientedwordsaboutproductsfromtexts(Khanetal.,2010).
Apr‐18‐12 30MarcirioChaves‐marcirioc@uatlan<ca.pt
Sen<mentAnalysis• Seedwords– areasmallsetofwordswithstrongnegaJveorposiJveassocia<ons,suchasexcellentorabysmal.
– Inprinciple,aposi<veadjec<veshouldoccurmorefrequentlyalongsidetheposi<veseedwords,andthuswillobtainaposi<vescore,whereasnega<veadjec<veswilloccurmosto\eninthevicinityofnega<veseedwords,thusobtaininganega<vescore(Taboadaet.al.2011).• Thisrestauranthasabadandexpensivefood.
Apr‐18‐12 31MarcirioChaves‐marcirioc@uatlan<ca.pt
Sen<mentAnalysis• Part‐of‐Speech(PoS)– Inordertoevaluateasentenceinareview,weshouldconsidertheparts‐of‐speechmen<onedsuchasadjecJves,adverbsandverbs.
– Adjec<vesareclassifiedas:• posi<ve(good,excellentandclean),• nega<ve(awful,boringandterrible),• neutral(regularandindifferent)and• dual,whichcanexpressposi<veandnega<veopinion(small,long).
– Insomeapproachesnounsarerepresentedbyconceptsofadomainontologyandmappedasfeatures.
Apr‐18‐12 32MarcirioChaves‐marcirioc@uatlan<ca.pt
Sen<mentAnalysis• ConjuncJonandConnecJve(CC)– Connec<vesarewordsthathelpiden<fyingaddiJonaladjecJveopinionwordsandtheirorientaJons.
– Oneoftheconstraintsisaboutconjunc<on(i.e.and),whichsaysthatconjoinedadjec<vesusuallyhavethesameorienta<on(Liu,2010).• Thisroomisbeau<fulandspacious.
– ifbeau<fulisknowntobeposi<ve,itcanbeinferredthatspaciousisalsoposi<ve.
– HeurisJc:• PeopleusuallyexpressthesameopiniononbothsidesofaconjuncJon.
Apr‐18‐12 33MarcirioChaves‐marcirioc@uatlan<ca.pt
Sen<mentAnalysis• ConjuncJonandConnecJve(CC)– Rulesorconstraintsarealsodesignedforotherconnec<ves(e.g.or,but,either‐or,andneither‐nor).• Thishotelisbeau<fulbutdifficulttogetthere.
– Theoccurrencea\ertheconnec<vebutisanindicatorofanega<veopinion.
Apr‐18‐12 34MarcirioChaves‐marcirioc@uatlan<ca.pt
Sen<mentAnalysis• StrengthofthePolaJryorIntensityorIntensificaJon– Amplifiers(very,alot)increasetheseman<cintensityofaneighboringlexicalitem;
– AXenuators/Downtoners(ali/le,slightly)decreaseit.
• SomeapproacheshaveimplementedintensifiersusingsimpleaddiJonandsubtracJon– ifaposi<veadjec<vehasanSOvalueof2:• anamplifiedadjec<vewouldhaveanSOvalueof3,and• adowntonedadjec<veanSOvalueof1.
Apr‐18‐12 35MarcirioChaves‐marcirioc@uatlan<ca.pt
Sen<mentAnalysis• NegaJon– Theobviousapproachtonega<onissimplytoreversethepolarityofthelexicalitemnexttoanegator,changinggood(+3)intonotgood(−3).
– Not,none,nobody,never,andnothing,andotherwords,suchaswithoutorlack.
Apr‐18‐12 36MarcirioChaves‐marcirioc@uatlan<ca.pt
PolarityRecognizerinPortuguese(PIRPO)• PolarityRecognizerinPortuguesetoclassifysenJmentin
onlinereviews.
• PIRPOwasbuiltfromthegroundtoPortugueseforrecognisingthepolarityoftheuseropiniononaccommoda<onreviews.
• Eachreviewisanalysedaccordingtoconceptsfromadomainontology.
• Wedecomposethereviewinsentencesinordertoassignapolaritytoeachconceptoftheontologyinthesentence.
Chaves,M.S.,Freitas,L.,Souza,M.andVieira,R.PIRPO:AnAlgorithmtodealwithPolarityinPortugueseOnlineReviewsfromtheAccommodaJonSector.17thInternaJonalconferenceonApplicaJonsofNaturalLanguageProcessingtoInformaJonSystems(NLDB),Groningen,TheNetherlands,26‐28June2012.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 37
PIRPOInforma<onArchitecture
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 38
PIRPO• Reviews– Fulldataset:1500reviewsfromJanuary2010toApril2011inPortuguese,EnglishandSpanish,fromwhich180inPortuguese.
• OntologyConcepts– TheconceptsusedtoclassifythereviewsareprovidedbyHontology,whichinitscurrentversion,has110concepts.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 39
PIRPO• ListofadjecJves:Itiscomposedbysen<ment‐bearingwords.– ThislistofpolaradjecJvesinPortuguese• contains30.322entries.• iscomposedbythenameoftheadjecJveandapolaritywhichcanassignoneofthreevalues:+1,‐1and0.
• ThesevaluescorrespondingtotheposiJve,negaJveandneutralsensesoftheadjec<ve.
– PIRPOusesthislisttocalculatethesemanJcorientaJonoftheconceptsfoundinthesentences.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 40
PIRPOAlgorithm
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 41
PIRPOMeasureEvalua<on
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 42
• Precision
• Recall
• F‐score(harmonicmeanofprecisionandrecall)
€
P ={relevantConcepts}∩{retrievedConcepts}
{retrievedConcepts}
€
R ={relevantConcepts}∩{retrievedConcepts}
{relevantConcepts}
€
F = 2 × P × RP + R
PIRPOPreliminaryResults
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 43
PIRPO:DiscussionontheResults• PIRPOreachedabe/errecallforconceptswithposi<vepolarity,whilemixedpolarityhadahigherprecision.
• ThelowF‐scorecanbemainlyduetothealgorithmhasassignedapolaritytoaspecificconceptoftheontology,whilethehumanclassifiedthereviewasawhole.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 44
OutlinePart1
• WorkshopContext
• User‐GeneratedContent(UGC)
• Characterisa<onofUGC• KnowledgeEngineering‐
OntologyDevelopment
• Hands‐onSession(IndividualTask):DealingwithUGC
Part2
• KnowledgeEngineering‐ModellingUGC
• Sen<mentAnalysis/OpinionMining
• PolarityRecognizerinPortuguese(PIRPO)
• Informa<onVisualisa<on
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 45
ContextWorkshop
AframeworkforCustomerKnowledgeManagementbasedonSocialSeman<cWeb.
Chaves,MarcirioSilveira;Trojahn,CássiaandPedron,Cris<aneDrebes.AFrameworkforCustomerKnowledgeManagementbasedonSocialSeman<cWeb:AHotelSectorApproach.In:CustomerRela<onshipManagementandtheSocialandSeman<cWeb:EnablingCliensConexus.Colomo‐Palacios,R.;Varajão,J.andSoto‐Acosta,P.(Eds.).p.141‐157,Hershey,PA:IGIGlobal,2012.ISBN:978‐161‐35‐0044‐6
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 46
Informa<onVisualisa<on• Whatisthevisualmodelofthepoten<alend‐user?
• Howshouldweproperlymapandrender:– themostvaluedaccommoda<onfeatures?
– thepercep<onofthequalityofferedbythehotel?– thecorrela<onbetweentheguest’sprofileandthemostlyrelevantfeatures?
– theintensityoftheposi<vityornega<vityofthefeatures?
• Doestheuseofadvancedvisualtechniques(suchastreeoriented)tomaptheresultswillhelptheaccommoda<onmanagersandgueststohaveabe/erinsightofthedata?
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 47
ExploringInforma<onVisualisa<on• Inthenextfigures
– ThecolorwasusedtomapthepolarityandthestrengthofthepolarityvaluesontheCO.
– ThesizewasusedtomapthefrequencythattheCOismen<onedinthereviews.
Apr‐18‐12 48MarcirioChaves‐marcirioc@uatlan<ca.pt
ExploringInforma<onVisualisa<on
Resultoftheapplica<onofBubbleTreevisualisaJonoftherela<onamongconceptsoftheontology,polarity(le\)and
strengthofthepolarity(right).
• Carvalho,E.;Chaves,M.S.,2012.ExploringUser‐GeneratedDataVisualizaJonintheAccommodaJonSector.16thInternaJonalConferenceInformaJonVisualisaJon,IEEE.(SubmiXed)
Apr‐18‐12 49MarcirioChaves‐marcirioc@uatlan<ca.pt
ExploringInforma<onVisualisa<on
Apr‐18‐12 50MarcirioChaves‐marcirioc@uatlan<ca.pt
ResultsusingTreemapvisualisaJonoftherela<onamongtypeofcustomer,conceptsoftheontologyandpolarity.
Ques<onnaire(inSpanish)• Youaregoingtoreceiveaques<onnaireaboutinforma<onvisualisa<onusingUGCinthecontextoftheaccommoda<onsector.
• Please,clickhereh/p://kwiksurveys.com?u=Infovisestoanswerit.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 51
FinalRemarks• In‐depthanalysisofUGCcanbeusedasinputtoimprovedecisionmaking.
• Itis<metothinkaboutnewmodelstostoreUGCdata.
• ItisnecessarythebuildingfromthegroundofnewalgorithmstodealwithUGCforlanguagesotherthanEnglish.
• InformaJonvisualisaJonofUGCisinitsinfancystate.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 52
MainReferences• S.Bethard,H.Yu,A.Thornton,V.Hatzivassiloglou,andD.Jurafsky,2004.Automa<cextrac<onofopinionproposi<onsand
theirholders.inProceedingsoftheAAAISpringSymposiumonExploringA%tudeandAffectinText.• Chesley,P.;Vincent,B.;Xu,L.andSrihariR.,2006.Usingverbsandadjec<vestoautoma<callyclassifyblogsen<ment.in
AAAISymposiumonComputa<onalApproachestoAnalysingWeblogs(AAAI‐CAAW),27–29.
• Ding,X.,Liu,B.,andYu,P.S.,2008.Aholis<clexicon‐basedapproachtoopinionmining.ProceedingsoftheConferenceonWebSearchandWebDataMining(WSDM).
• M.HuandB.Liu,2004.Miningopinionfeaturesincustomerreviews.InProceedingsofAAAI,pp.755–760.
• S.‐M.KimandE.Hovy,2004.Determiningthesen<mentofopinions.InProceedingsoftheInterna.onalConferenceonComputa.onalLinguis.cs(COLING),2004.
• Liu,Bing,2010.Sen<mentAnalysisandSubjec<vity.InHandbookofNaturalLanguageProcessing,SecondEdi<on,Eds:N.IndurkhyaandF.J.Damerau),CRCPress,TaylorandFrancisGroup,BocaRaton,FL.Chapter28.
• Mar<n,J.R.andWhite,P.R.R.,2005.TheLanguageofEvalua<on,AppraisalinEnglish,PalgraveMacmillan,London&NewYork.
• Taboada,M.,Brooke,J.,Tofiloski,M.,Voll,K.D.,Stede,M.,2011.Lexicon‐basedmethodsforsen<mentanalysis.Computa<onalLinguis<cs37(2),267–307.
• Tang,H.,Tan,S.,Cheng,X.,2009.Asurveyonsen<mentdetec<onofreviews.ExpertSystemswithApplica<ons36(7),10760–10773.
• Whitelaw,C.;Garg,N.andArgamon,S.,2005.Usingappraisalgroupsforsen<mentanalysis.InProceedingsofthe14thACMinterna<onalconferenceonInforma<onandknowledgemanagement(CIKM'05).ACM,NewYork,NY,USA,625‐631.
• Wilson,T.,2008.Fine‐GrainedSubjec<vityAnalysis.PhDDisserta<on,IntelligentSystemsProgram,UniversityofPi/sburgh.
• Wilson,T.,Wiebe,J.,Hoffmann,P.,2009.Recognizingcontextualpolarity:Anexplora<onoffeaturesforphrase‐levelsen<mentanalysis.Computa<onalLinguis<cs35,399–433.
• Y.Wu,F.Wei,S.Liu,N.Au,W.Cui,H.Zhou,andH.Qu,2010.OpinionSeer:Interac<veVisualisa<onofHotelCustomerFeedback.IEEETransac<onsonVisualiza<onandComputerGraphics,6,1109‐1118.Nov‐Dec.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 53
Open‐sourcesen<ment‐analysistools• PythonNLTK(NaturalLanguageToolkit)– h/p://www.nltk.organdh/p://text‐processing.com/demo/sen<ment
• R,TM(textmining)moduleh/p://cran.r‐project.org/web/packages/tm/index.html
• RapidMinerh/p://rapid‐i.com/content/view/184/196/
• GATE,theGeneralArchitectureforTextEngineeringh/p://gate.ac.uk/sen<ment
• UIMA‐plug‐inannotatorsforsen<ment—ApacheUIMAistheUnstructuredInforma<onManagementArchitecture,h/p://uima.apache.org/
• SenJmentclassifiersfortheWEKAdata‐miningworkbench,h/p://www.cs.waikato.ac.nz/ml/weka/.
• StanfordNLPtools‐h/p://www‐nlp.stanford.edu/so\waremaximum‐entropyclassifica<onapproachforsen<ment.
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 54
Thankyouverymuchforyoura/en<on!!
Ques<ons
Apr‐18‐12 MarcirioChaves‐marcirioc@uatlan<ca.pt 55
top related