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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳政鴻 | zh_TW |
dc.contributor.advisor | Cheng-Hung Wu | en |
dc.contributor.author | 吳珍瑋 | zh_TW |
dc.contributor.author | Chen-Wei Wu | en |
dc.date.accessioned | 2024-03-05T16:13:17Z | - |
dc.date.available | 2024-03-06 | - |
dc.date.copyright | 2024-03-05 | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-02-16 | - |
dc.identifier.citation | Anita Elberse, & Eliashberg, J. (2003). Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures. Marketing Science, 22(3), 329–354.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92086 | - |
dc.description.abstract | 電影欣賞是日常中極為普遍的娛樂方式,然而,隨著新冠肺炎疫情在世界各地爆發,人們的生活及消費模式改變,電影市場亦受到強烈衝擊,全球票房大幅衰退,若能提前預測電影票房,便能讓製片公司、發行商和投資者更容易決策。因此,本研究探討疫情爆發前後兩年(2018~2021)臺灣電影市場發展與變化。
本研究樣本為2018~2021年於臺灣上映之美國電影374部,結合電影基本資料、競爭關係、經濟因素與疫情因素預測電影票房。採用留一交叉驗證評估,並應用sci-kit learn中的線性迴歸、支持向量迴歸、決策樹與隨機森林迴歸,四種預測技術建立電影票房預測模型,評估指標包含R平方、平均絕對誤差與均方根誤差,結果顯示隨機森林迴歸預測效果最好,但疫情後較難預測,此外,本研究進一步探討疫情前後影響電影票房的特徵重要性分析。 結果顯示,疫情前預算特徵為最重要因素,疫情後首週放映院數為最重要之因素,預算特徵在疫情期間影響力大幅下降,動作類型環境因子影響力上升,意即動作類型電影在疫情期間競爭關係相對增加,此外,娛樂稅資料與疫情確診人數及死亡人數具一定程度關聯,疫情後娛樂稅之電影占比影響力上升,亦可代表電影票房受疫情環境影響。 | zh_TW |
dc.description.abstract | Movie appreciation is an extremely common form of entertainment in daily life. However, as the COVID-19 epidemic breaks out around the world, people''s lives and consumption patterns have changed, and the movie market has also been strongly impacted. The global box office has declined sharply. If the movie box office can be predicted in advance, This will make decision-making easier for production companies, distributors and investors. Therefore, this study explores the development and changes of the Taiwan film market in the two years before and after the outbreak of the epidemic (2018~2021).
The sample of this study is 374 American movies released in Taiwan from 2018 to 2021. The movie box office is predicted based on basic movie information, competitive relationships, economic factors and epidemic factors. Use leave-one-out cross-validation evaluation, and apply linear regression, support vector regression, decision tree and random forest regression in sci-kit learn. Four prediction technologies are used to establish a movie box office prediction model. The evaluation indicators include R square, mean absolute error and root mean square error, the results show that random forest regression has the best prediction effect, but it is more difficult to predict after the epidemic. In addition, this study further explores the importance analysis of features that affect movie box office before and after the outbreak of epidemic. The results show that the budget characteristics before the epidemic are the most important factor, and the number of theaters in the first week after the epidemic is the most important factor. The influence of budget characteristics dropped significantly during the epidemic, and the influence of action type environmental factors increased, which means that the impact of action movies during the epidemic period competition has increased relatively. In addition, entertainment tax data has a certain degree of correlation with the number of confirmed cases and deaths of the epidemic. After the epidemic, the influence of the entertainment tax on movies has increased, which can also mean that movie box office is affected by the epidemic environment. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-03-05T16:13:16Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-03-05T16:13:17Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 致謝i
摘要ii ABSTRACTiii 目錄iv 圖目錄vii 表目錄ix 第一章 緒論1 1.1 研究背景1 1.2 研究動機與目的1 1.3 研究流程3 第二章 文獻回顧4 2.1 電影票房預測的相關研究4 2.2 疫情對電影產業的影響7 第三章 研究方法9 3.1 資料來源9 3.2 敘述統計分析9 3.2.1 臺灣電影市場各週總票房波動9 3.2.2 臺灣電影市場個別電影之總票房表現10 3.2.3 臺灣市場個別電影之變數分佈11 3.2.4 美國電影於臺灣市場之總票房表現12 3.2.5 在臺上映美國電影之變數分佈14 3.3 變數探討14 3.3.1 電影類型14 3.3.2 電影分級15 3.3.3 續集電影16 3.3.4 電影預算16 3.3.5 上映院數17 3.3.6 假期18 3.3.7 競爭因素18 3.3.8 經濟因素19 3.3.9 疫情因素19 3.4 機器學習方法20 3.4.1 線性迴歸20 3.4.2 支持向量迴歸20 3.4.3 決策樹21 3.4.4 隨機森林21 3.5 資料驗證與模型評估22 3.5.1 交叉驗證22 3.5.2 評估指標22 3.6 研究架構23 第四章 研究結果24 4.1 網路爬蟲24 4.2 資料前處理24 4.2.1 資料刪除24 4.2.2 資料填補25 4.2.3 資料合併25 4.3 資料特徵26 4.3.1 應變數26 4.3.2 自變數27 4.4 實驗結果27 4.5 特徵重要性31 4.6 決策樹分析36 4.6.1 疫情前(2018~2019)決策樹規則36 4.6.1 疫情後(2020~2021)決策樹規則38 第五章 結論與建議40 5.1 研究結論40 5.2 研究限制42 5.3 未來研究建議43 參考文獻44 參考資料47 附錄48 | - |
dc.language.iso | zh_TW | - |
dc.title | 後疫情時代之美國電影臺灣票房預測與分析 | zh_TW |
dc.title | Post-Pandemic Box Office Prediction and Analysis of American Films in Taiwan | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 陳文智;周育樂 | zh_TW |
dc.contributor.oralexamcommittee | Wen-Chih Chen;Ywh-Leh Chou | en |
dc.subject.keyword | 美國電影,票房預測,新冠肺炎,機器學習,隨機森林, | zh_TW |
dc.subject.keyword | American movie,box office prediction,COVID-19,machine learning,random forest, | en |
dc.relation.page | 53 | - |
dc.identifier.doi | 10.6342/NTU202400597 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2024-02-16 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 工業工程學研究所 | - |
顯示於系所單位: | 工業工程學研究所 |
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