服务质量和顾客价值外文翻译文献.docx

服务质量和顾客价值外文翻译文献.docx

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服务质量和顾客价值外文翻译文献

文献信息

文献标题：

AMethodforOpinionMiningofCoffeeServiceQualityandCustomerValuebyMiningTwitter（基于推文挖掘的咖啡服务质量和顾客价值的观点探勘）

文献作者及出处：

TakahashiS,SugiyamaA,KohdaY.Amethodforopinionminingofcoffeeservicequalityandcustomervaluebyminingtwitter[M]//Knowledge,InformationandCreativitySupportSystems.Springer,Cham,2016:

521-528.

字数统计：

英文1867单词，9106字符；中文3059汉字

外文文献　

AMethodforOpinionMiningofCoffeeServiceQualityandCustomerValuebyMiningTwitter

AbstractInthiswork,wefocusoncustomervalueofJapanesecoffeeservicebyanalyzingthehugetweetdataset.Wesuggestthequantitativeevaluationmethodofcustomervaluebyusingthemorphologicalanalysisofthetweetsandgeneratedvaluedictionary.Oursuggestedmethodologycanbeappliedtoanyserviceandproducttoimprovethecommoditizationproblemfromtheviewpointofservicescience.

Keywords:

Customervalue;Naturallanguage;processingService;valueOpinionmining

1.Introduction

Recently,bigdatautilizationinsocialnetworkdata（e.g.Facebook,Twitter,Flicker）hasattractedmuchattentionfromresearchersandresearchdivisionsofcompaniesinordertoanalyzethemarketwithhigh“objectivity”andhigh“completeness”.Inthisstudy,wefocusontheTwitterdatafromtheviewpointofcustomersvalueofproductsandservicesinthecommoditizationofservice.Specifically,weexaminecoffeeserviceinJapanasanexampleofcommoditization.

Demandforcoffeebeanshasbeenincreasing,andcoffeeprovisioningmethodbecomingmorediversifyintheworldcoffeemarketsandservice.Inthedecadefromtheyear2000,coffeebeanconsumptionintheimportingcountrieshasincreasedtomorethan8%.Inaddition,coffeebeansconsumptionintheexportingcountrieshasincreasedby48%ormoreWiththeadventofaconveniencestorecoffee,acompetitioninthecoffeemar-kethasintensifiedinJapan.Thetotalnumberofsalesofconveniencestorecoffeehasincreasedto500millioncupsinlessthanoneyearfromthestartofsalesin2012.CoffeeconsumptioninJapanwas440thousandtonsin2013.Thisnumberincreasedby4.3%fromthepreviousyearbecauseofthehitofaconveniencestorecoffee.Therefore,commoditizationofSeattle-typecoffeeandconveniencestorescoffeehasprogressedintensifyotherresultofpricecompetition.Inaddition,anewtypeofcoffeeshopcalledThird-waveappearedaroundthewestcoastofNorthAmer-ica,andisspreadingallovertheworld.

ControllingcommoditizationincoffeeserviceisimportantforcoffeemarketnotonlyJapan,butalsowithintheemergingcountries.Inthisstudy,weexplorethepossibilitytocontrolthecommoditizationincoffeeservice.Therefore,weanalyzethevaluesofthecustomerswithinamongtheSeattle-typecoffeeandconveniencestorecoffee,toclarifytheneedsofthecustomersforcoffeeservice.

2.ProblemStatement

Thetypeofcoffeeserviceisclassifiedintothreecategories.Thefirstoneisaconvenient-store-stylecoffeethatisprovidedbyconveniencestores（CVS）andfastfoodshops（e.g.MaccafebyMcDonalds）.Thiskindofcoffeehastheauthenticflavorandisinexpensiveprice.Thesecondtypeiscafeserviceprovidingrelaxingplace.ManynumberofSeattle-typecoffeepresentsuchkindofservice（e.g.TullysCoffee,StarbucksCoffee）.Amongthese,byprovidingthethirdplacenoratworkorathome,thelargechainstoreStarbuckshavesucceededintosatisfytheneedsofcus-tomers.ThethirdtypeisaThird-wavecoffee.Third-wavecoffeeisonlyusessingleorigincoffeebeansthatwereharvestedfromseedlingsofasinglespecies,andismadebydrip.

InJapan,intensificationofpricecompetitivenesshasproceedingbetweenSeattle-typecoffeeandconveniencestorescoffee.Inthisstudy,weanalyzebythreepoints“taste”,“price”and“place”,thevaluesofthecustomerstotheCVSandStarbucks.UsingthetweetsofcustomersobtainedfromtheTwitterStreamingAPIdata,tobeanalyzedbythefollowingprocedure.

（i）ExtracttheJapanesetweetsthatarerelatedtocoffeeserviceusingtheTwitterStreamingAPI.

（ii）Generatedictionaryabout“taste”,“price”and“place”bytheJapanesetweetsextracted.

（iii）Mapafeaturespaceof“taste”,“price”and“place”aboutCVSandStarbucksusingthegenerateddictionary.

（iv）Fitofthestatisticalmethodstoanalyzetweetvectorsinthefeaturespace.

3.DataCollectionandFeatureExtraction

Inordertoextractthesenseofvaluesfromthetweetsofcustomers,wecreatedadictionaryaboutsenseofvalues（valuedictionary）relatedto“taste”,“price”and“place”.Inthedictionaryabout“taste”,“price”and“place”havebeenproduced,thetopfiveofthewordsareshowninTable1.

ExtractthetweetsaboutCVSandStarbucksamong118,855targettweets.Amongthetweets,wedefinedasCVSthatcontains[,convenience],andStarbucksthatcon-tains[,,Starbucks].Table2showsthecountsoftweetsrelatedtoCVSandStarbucksextracted.Thetargetdatais5,141tweetsoutof118,855Japanesetweetsaboutthecoffee.

WerepresentthetweetsofthetargetdatabyaVectorSpaceModelbasedonthevaluedictionary.AstheNwordspresentedinthevaluedictionarysuchasV1,V2,V3,…,VNtweetTisexpressedastweetvectortsuchasthefollowing.

Table1Thetop5frequentwordstodefinetheofdictionaryfromthetweetdatarelatedtocoffeetweet

Table2Countsoftweets

Class

Count

Starbucks

3612

CVS

1529

Total

5141

Inthesubsequentanalysis,weanalyzefromtweetvectors,whicharemappedtothefeaturespace.

4.Analysis

Ifcustomersarefocusedonvaluerelatedto“taste”,itcanbeexpectedthatthetweetvectorsincludevalueinthe“taste”dictionary.Wecomparevalueon“taste”ofcus-tomersaboutCVSandStarbucksthroughthetargettweets.Table3showsthenumberoftweetvectorscontainingthefeaturespacethatisgeneratedfromthedictionary.

WetestwithdifferenceofpopulationproportionusingthePearson’s𝜒2testsontheTable3a.FromtheresultsofthePearson’s𝜒2tests,therewasnodifferenceinthepopulationproportionatlevelofsignificance0.05.Becausethereisnodifferenceinthepopulationproportionoftweetrelatedtotaste,itcanbesaidthatconsumersarefocusedinthesamewayasCVSandStarbuckson“taste”.

Ontheotherhand,wefindthedifferenceinPearson’s𝜒2testson“price”and“place”atlevelofsignificance0.05showninTable3b,c.TheseresultsindicatethatCVScustomersarefocusedmorestronglyon“price”thanStarbuckscustomers.

Table3Thenumberoftweetsrelatedtothreecategories

F

T

Total

（a）Price

CVS

1280

249

1529

Starbucks

3017

595

3612

Total

4297

844

5141

（b）Place

CVS

1344

185

1529

Starbucks

3327

285

3612

Total

4671

470

5141

（c）Taste

CVS

1216

313

1529

Starbucks

2722

890

3612

Total

3938

1204

5141

Ontheotherhand,Starbuckscustomersarefocusedmorestronglyon“place”thanCVScustomers.

Weanalyzethatthecustomersarefocusedonwhatkindofvalueon“place”whentheybuyCVSorStarbucks.Table4showsfivewordswithagreatdifferenceatlevelofsignificance0.05inthepopulationproportion,whichcorrespondstothevaluedictionaryaboutplace.

Table4Differenceinthepopulationproportion

TherearemanypositivewordsforStarbuckscoffee,suchas“happy”,“high-quality”and“fashionable”.ItisevaluatedthattheplaceofferingStarbucksisahighqualitycomparedtotheCVS,andcustomersaresatisfied.Inaddition,manywordsaboutGenerationlike“adult”and“youth”haveappeared.

Aboveall,itisseenfromgenerationwordsappearedfromStarbuckstweetvectorsthatthirdplacehasbeenevaluatedfornotonlyequipmentbutalsogenerationsandpeoplewhogatherthere.Incontrast,theplaceofCVShasnotbeenevaluated,itisfoundthatcustomersarenotinterestedintheplacefordrinkingCVS.

Recently,positive-negativejudgementofSNStextbymachinelearningtechnicessuggestsomeusefulpredictionaboutmarketandpoliticaltrends.Takumuraetal.providetheJapanesesemanticorientationsofwordsbyconstractthelexicalnetwork.Intheirstudy,theytheemotionwastreatedasthespinofelectronsandsucceededinextractingsemanticorientationswithhighaccuracy,evenwhenonlyasmallnumberofseedwordsareavailable.Inourstudy,weestimatethesentimentalcoeffcientofcustumersbyJapanesesemanticorientationsoftweetwords.

SentimentalcoeffcientS（T）oftweetTiswrittenbyberow.

Here,scoreL（ai）andfrequencyF（ai）arecalculatedbyscoreofaverb,anounandanadjactivephrasea1,a2,a3,…,ai,…,atinsemanticorientationmap.L（ai）valueisfrom−1（negative）to1（positive）.Distributionmapofsentimentalcoeffcientabout“price”and“place”indictionaryareshowninFig.1.

Fig.1Distributionmapofsentimentalcoefficient

Moreover,significantdifferencearenotobservedintheresultsoft-testaboutsentimentalcoefficientof“price”and“place”showninTable5.TheseresultsindicatethecastumervalueevaluatebynotonlysentimentalcoefficientofSNStextdata,butalsothequanitativeanalysisoftweetisimportantforconsidertheservicevalue.

Table5Sentimentalcoefficientof“price”and“place”

5.Discussion

Inthisstudy,wediscussthecustomersvalueofcoffeeservicebyanalyzingthehugetweetdataset.Wesuggestthequantitativeevaluationmethodofcustomer’svaluebyusingthemorphologicalanalysisofthetweetsandgeneratedvaluedictionary.

Thecharacterizesofallserviceandproductcanbeclassifiedintothreequalities:

searchqualities,experiencequalitiesandcredencequalities.Withcommoditization,thevalueofproductandservicefallintothesearchqualities.Originally,a