在无处不在的社交媒体数据中自动发现产品偏好：汽车市场的案例研究外文翻译(中英文)

在无处不在的社交媒体数据中自动发现产品偏好：汽车市场的案例研究摘要-社交媒体可以实现无处不在的沟通，使用户可以随时随地传播和接收信自不同的地理区域。处理[12]，这样的产品相关信息可以在产品评论网品的信息。未实现，这可能是用于在客户上市之前预测客户对产品需求的体面信号。的信息包括网站提供的交易和其他客户服务的经验。三方利益共享公司已经使用BestB和A等的竞争对手的产品在客户心中[14]。取由数据收集器指定的产品审查产品，中一些是直接或间接的具体产品或产品特征[17]。社交媒体数据的这种异质性意见限制:虽然与产品评论网站中的产品相关的评论通常由已经购买产品的客户这种方法推广到更广泛的产品,特别是像汽牢和其他汽车产品等与社交媒体平台的使用没有紧密的联系时，常常会产生疑虑。品相关的用户的集体情绪能否成为实际客户对这种产品的需求的指获客户的兴趣[20]。例如，汽车的产品特征包括天然气，行驶，越野和价格。产品特征可能是强大保留(如果已经存在)或在下一代产品中合成。另一方面，薄弱的特征不能满足客户的需求，因此应该被删除(如果已经存在)或不包括在下一代产品中。一1.量化产品的时间需求。社会媒体有影响客户购买决定的说服力(也称为“嗡嗡2.发现与产品成功或失败相关的显着产品特征。我们扩展了[16]中使用的技术来情绪的能力。有了这样的假设，客户对特定产品/产品特征的偏好可以从他们在该算法使用机器翻译喜欢的技术将消息中的每个单词/符号映射到数字值。然后。情感强度(ES)=负面分数-正面分数(1)然后，情绪强度(ES)用于确定与特定产品/产品功能相关的产品相关消息的情/中性(0ve)[16]。A.预测汽车需求本节介绍使用社交网络服务(如Facebook和Twitter)提取的社交媒体数据的方Twitter数据的积极情绪是被证明是票房收入强大预测因素的三个因素之一[23]。的+ve消息的数量正数用于推断最终用户的集体需求。显们复制他们的方法并将其应用于不同的产。veveve预处理层，包括z有争议(t)=P(t|s+，D，T)×P(t|s-，D，T)(2)有名词术语被过滤为候选产品特意标准精确度@50=|前50项中的特征项|/50(3)A.预测汽车需求本节介绍了展示社会媒体数据挖掘以量化产品预售产品需求的可行性的结果。告的实际产品销售数字进行比较来验证体消息预测四分之一的销售。例如，为了预测一个月的销售额(M=1)，2013售预测模型-六月)。三分之一窗口偏移(QWS)用于定义四分之一窗口的向后中报道。对这种现象的解释可能是，智能手机用户通过使用智能手机(特别是Twitter应用程序)来表达自己的经验，这比汽车消费者能是至关重要的。具箱[44]从大规模的社交媒体消息语料库生创性的越野功能而闻名，客户抱怨其不舒服的座椅和响亮的发动机噪声。从社交媒体数据中提取汽车产品的显着(弱/强/有争议)特征。这些信息可以的。[1]S.TuarobandC.S.Tucker,“Quantifyingproductfavorabilityandextractingnotableproductfeaturesusinglargescalesocialmediadata,”JournalofComputingandInformationScienceinEngineering,vol.15,no.3,p.031003,2015.[2]——,“Automateddiscoveryofleadusersandlatentproductfeaturesbymininglargescalesocialmedianetworks,”JournalofMechanicalDesign,vol.137,no.7,p.071402,2015.[3]W.JankandP.Kannan,“Understandinggeographicalmarketsofonlinefirmsusingspatialmodelsofcustomerchoice,”MarketingScience,vol.24,no.4,pp.623–634,2005.[4]J.Hyde,“Themostpopularnewvehicleineachstate?Notwhatyoumightexpect,”/autos/bp/the-most-popularnew-vehicle-in-each-state–not-what-you-might-expect-181118005.html,2015,[Online;accessed9-March-2016].[5]N.CollierandS.Doan,“Syndromicclassificationoftwittermessages,”inElectronicHealthcare,ser.LectureNotesoftheInstituteforComputerSciences,SocialInformaticsandTelecommunicationsEngineering,2012,vol.91,pp.186–195.[6]T.Sakaki,M.Okazaki,andY.Matsuo,“Earthquakeshakestwitterusers:real-timeeventdetectionbysocialsensors,”inProceedingsofthe19thinternationalconferenceonWorldwideweb,ser.WWW’10,2010.[7]C.Caragea,N.McNeese,A.Jaiswal,G.Traylor,H.Kim,P.Mitra,D.Wu,A.Tapia,L.Giles,B.Jansenetal.,“Classifyingtextmessagesforthehaitiearthquake,”inISCRAM11.[8]A.Cui,M.Zhang,Y.Liu,S.Ma,andK.Zhang,“Discoverbreakingeventswithpopularhashtagsintwitter,”ser.CIKM’12,2012.[9]J.Bollen,H.Mao,andX.Zeng,“Twittermoodpredictsthestockmarket,”JournalofComputationalScience,vol.2,no.1,pp.1–8,2011.[10]A.Tumasjan,T.O.Sprenger,P.G.Sandner,andI.M.Welpe,“Predictingelectionswithtwitter:What140charactersrevealaboutpoliticalsentiment,”inICWSM’10,2010,[11]S.Tuarob,C.S.Tucker,M.Salathe,andN.Ram,“Modelingindividuallevelinfectiondynamicsusingsocialnetworkinformation,”inProceedingsofthe24thACMInternationalonConferenceonInformationandKnowledgeManagement.ACM,2015,pp.1501–1510.[12]C.TuckerandH.Kim,“Trendminingforpredictiveproductdesign,”TransactionsoftheASME-R-JournalofMechanicalDesign,vol.133,no.11,p.111008,2011.[13]A.M.KaplanandM.Haenlein,“Usersoftheworld,unite!thechallengesandopportunitiesofsocialmedia,”BusinessHorizons,vol.53,no.1,pp.59–68,2010.[14]G.Fei,A.Mukherjee,B.Liu,M.Hsu,M.Castellanos,andR.Ghosh,“Exploitingburstinessinreviewsforreviewspammerdetection,”inAAAIICWSM2013,2013.[15]J.H.Kietzmann,K.Hermkens,I.P.McCarthy,andB.S.Silvestre,“Socialmedia?getserious!understandingthefunctionalbuildingblocksofsocialmedia,”BusinessHorizons,vol.54,no.3,pp.241–251,2011.[16]S.TuarobandC.S.Tucker,“Fadorheretostay:Predictingproductmarketadoptionandlongevityusinglargescale,socialmediadata,”inASME2013IDETC/CIE.ASME,2013.[17]E.Agichtein,C.Castillo,D.Donato,A.Gionis,andG.Mishne,“Findinghigh-qualitycontentinsocialmedia”ser.WSDM’08.NewYork,NY,USA:ACM,2008,pp.183–194.[18]S.TuarobandC.S.Tucker,“Aproductfeatureinferencemodelforminingimplicitcustomerpreferenceswithinlargescalesocialmedianetworks,”inASME2015IDETC/CIE.ASME,2015.[19]——,“Discoveringnextgenerationproductinnovationsbyidentifyingleaduserpreferencesexpressedthroughlargescalesocialmediadata,”inASME2014IDETC/CIE.ASME,2014.[20]K.Zhang,R.Narayanan,andA.N.Choudhary,“Voiceofthecustomers:Miningonlinecustomerreviewsforproductfeature-basedranking.”WOSN,vol.10,pp.11–11,2010.[21]M.Thelwall,K.Buckley,G.Paltoglou,D.Cai,andA.Kappas,“Sentimentinshortstrengthdetectioninformaltext,”J.Am.Soc.Inf.Sci.Technol.,vol.61,no.12,pp.2544–2558,Dec.2010.[22]E.Fox,Emotionscience:cognitiveandneuroscientificapproachestounderstandinghumanemotions.PalgraveMacmillan,2008.[23]S.AsurandB.A.Huberman,“Predictingthefuturewithsocialmedia,”inProceedingsofthe2010IEEE/WIC/ACMInternationalConferenceonWebIntelligenceandIntelligentAgentTechnology-Volume01,ser.WIIAT’10.Washington,DC,USA:IEEE,2010,pp.492–499.[24]S.ChungandS.Liu,“Predictingstockmarketfluctuationsfromtwitter,”2011.[25]B.OConnor,R.B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