博碩士論文 101421042 完整後設資料紀錄

DC 欄位 語言
DC.contributor企業管理學系zh_TW
DC.creator謝翔安zh_TW
DC.creatorXiang-An Xieen_US
dc.date.accessioned2014-6-27T07:39:07Z
dc.date.available2014-6-27T07:39:07Z
dc.date.issued2014
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=101421042
dc.contributor.department企業管理學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract由於電影工業的蓬勃發展,劇院聲光效果持續不斷進步,電影已是現代人不可或缺的重要休閒娛樂方式。但製作一部商業電影需要花費高額成本及面臨高風險,有許多電影上映後票房收入遠低於成本而造成鉅額虧損,是否可以賺取利潤儼然成為電影產業的重要考量因素。但由於電影上映之前很難評估該電影的品質及觀眾的接受程度,而電影製作公司在電影拍攝前就必須決定成本及各方面考量的情況下,分析及預測未上映的電影變得格外重要。本研究透過蒐集過去十年電影資料,包含電影各種屬性及觀眾評價,使用多元線性迴歸分析(multiple linear regression analysis)、凸組合(convex combination)以及類神經網路(artificial neural network)預測未上映電影觀眾評價,讓投資者及電影製作公司在拍攝電影前作為決策的參考依據。zh_TW
dc.description.abstractDue to the rise of the film industry, movies have been an essential and important recreation to human beings. But making a commercial film need high production costs and face high risks, the gross of many movies are usually far less than the costs, which causes huge losses. Therefore, the engagement of a forecast in box office income and cost is deemed to be an important issue to many scholars and the industry members. However, it’s difficult to evaluate the quality of the movie and acceptability of moviegoers before the movie is released, the film production company must determine the cost and various aspects before filming. This research collected movie data from over the past decade, which includes various attributes of movies and moviegoer’s evaluations, and uses multiple linear regression analysis method, convex combination and artificial neural network method to forecast unreleased moviegoer’s evaluations, so that investors and film production companies can have a reference basis whilst making decisions before filming. en_US
DC.subject電影評價預測zh_TW
DC.subject多元線性迴歸分析zh_TW
DC.subject凸組合zh_TW
DC.subject類神經網路zh_TW
DC.subjectforecast film evaluationen_US
DC.subjectmultiple linear regressionen_US
DC.subjectconvex combinationen_US
DC.subjectartificial neural networken_US
DC.title根據電影屬性預測所獲評分之研究zh_TW
dc.language.isozh-TWzh-TW
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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