根據電影屬性預測所獲評分之研究

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姓名

謝翔安(Xiang-An Xie) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

根據電影屬性預測所獲評分之研究

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摘要(中)

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。

摘要(英)

Due 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.

關鍵字(中)

★ 電影評價預測
★ 多元線性迴歸分析
★ 凸組合
★ 類神經網路

關鍵字(英)

★ forecast film evaluation
★ multiple linear regression
★ convex combination
★ artificial neural network

論文目次

中文摘要 ………………………………………………………………… I
Abstract ………………………………………………………………… II
目錄 ………………………………………………………………… III
圖目錄 ………………………………………………………………… IV
表目錄 ………………………………………………………………… V
第一章緒論…………………………………………………………… 1
1-1 研究背景與動機…………………………………………… 1
1-2 研究目的…………………………………………………… 3
1-3 論文架構…………………………………………………… 3
第二章文獻探討……………………………………………………… 4
2-1 Internet Movie Database (IMDb)…………………………… 4
2-2 電影票房及相關預測……………………………………… 4
2-3 凸組合……………………………………………………… 5
2-4 多元線性迴歸分析………………………………………… 6
2-5 類神經網路………………………………………………… 7
第三章研究方法……………………………………………………… 9
3-1 研究流程…………………………………………………… 9
3-2 資料蒐集…………………………………………………… 10
3-2-1 資料來源………………………………………………… 10
3-2-2 資料說明………………………………………………… 10
3-3 屬性篩選…………………………………………………… 11
3-4 資料處理…………………………………………………… 12
3-4-1 離群值處理……………………………………………… 12
3-4-2 資料轉換………………………………………………… 13
3-4-3 計算電影所含屬性分數………………………………… 14
3-5 建立預測模型……………………………………………… 19
3-5-1 凸組合…………………………………………………… 19
3-5-2 多元線性迴歸分析……………………………………… 21
3-5-3 類神經網路……………………………………………… 24
第四章資料分析與研究結果………………………………………… 26
4-1 計算模型預測誤差………………………………………… 26
4-2 研究結果…………………………………………………… 26
4-3 討論………………………………………………………… 28
第五章結論與建議…………………………………………………… 30
5-1 結論………………………………………………………… 30
5-2 研究限制…………………………………………………… 30
5-3 未來研究方向……………………………………………… 31
參考文獻 ………………………………………………………………… 32

參考文獻

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指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2014-6-27

推文