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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦 | |
| dc.contributor.author | Cheng-Yu Wu | en |
| dc.contributor.author | 吳承宇 | zh_TW |
| dc.date.accessioned | 2021-06-08T06:59:27Z | - |
| dc.date.copyright | 2011-08-16 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-15 | |
| dc.identifier.citation | [1] N. Askitas and K. F. Zimmerman, “Google econometrics and unemployment forecasting,” Applied Economics Quarterly, vol.55, pp. 107-120, 2009.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/26063 | - |
| dc.description.abstract | 銷售預測往往影響著組織的管理與策略的制定，準確的銷售預測能使企業能夠保持競爭優勢，而銷售預測的誤差會對企業造成相當大的影響。在資訊科技發達及 WEB 2.0 興盛的年代，數位口碑及網路行為能表達消費者的興趣及趨勢，過去口碑與銷售量影響的研究均針對商品的數位口碑進行分析，從中取得口碑與銷售量的關係來預測銷售量，本研究則強調以消費者主動的角度，利用群眾智慧的力量以及數位口碑來讓銷售量的預測更為準確，我們利用搜尋記錄(Query Log)代表消費者的興趣及主動性，透過 Google 的 Google Insights for Search 服務來取得使用搜尋記錄，並將查詢值結合了時間序列(Time Series)的貝斯模型(Bass Model)來預測電影票房，利用過去三年的電影資料，我們的研究證明了搜尋記錄的確有助於電影票房的銷售預測，而研究中也驗證了搜尋記錄在電影票房銷售預測中的重要性。 | zh_TW |
| dc.description.abstract | Sales forecasting is essential to decisions making and processes planning. Any error of sales forecasting will affect the competitive advantage of corporations. In the era of Web2.0, word-of-mouth and network behaviors are representative of consumer interests. In this study, we study word-of-mouth effect and the power of wisdom of crowds for sales forecasts. We also incorporate the query logs of search engines into a time series based Bass Model to construct a sales prediction model which produce accurate sales forecasting. Evaluations based on a movie dataset demonstrate that the query logs of search engines are effective in box office predictions. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T06:59:27Z (GMT). No. of bitstreams: 1 ntu-100-R98725021-1.pdf: 760700 bytes, checksum: ef4c5d7a579a9c24ee2cfd239d15854e (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 謝詞 i
論文摘要 ii THESIS ABSTRACT iii 目錄 v 圖目錄 vii 表目錄 viii 第一章 緒論 1 I. 研究動機 1 II. 研究目的 2 III. 研究範圍 3 IV. 研究架構 3 第二章 相關研究文獻探討 4 I. 貝斯擴散模型（The Bass Model） 4 1. 創新擴散理論 4 2. 貝斯模型 5 3. 貝斯模型的參數估計 7 II. 電影票房預測 8 III. 時間序列（Time Series） 9 1. 時間序列 9 2. 時間序列的概念在貝斯模型上的應用 9 IV. Google搜尋透視 (Google Insights for Search) 10 1. Google搜尋透視 10 2. 詞彙查詢資料(Query Logs)的應用 10 第三章 研究模型 12 I. 銷售量預測模型(Sales Forecasting Model) 12 II. 市場潛量預測模型(Market Potential Estimation Model) 14 第四章 實驗 18 I. 電影資料庫 18 1. Yahoo! Movies 18 2. Box Office Mojo 19 3. 電影資料蒐集 20 II. 實驗方法及結果 22 III. 其他群眾智慧因素驗證 29 IV. 模型分析 32 第五章 結論 34 參考資料 35 | |
| dc.language.iso | zh-TW | |
| dc.subject | 電影票房預測 | zh_TW |
| dc.subject | 文字探勘 | zh_TW |
| dc.subject | 查詢記錄分析 | zh_TW |
| dc.subject | Text Mining | en |
| dc.subject | Box office Forecast | en |
| dc.subject | Query Log Analysis | en |
| dc.title | 以搜尋引擎查詢紀錄為基礎之電影票房研究 | zh_TW |
| dc.title | A Study of Box Office Prediction Using the Query Logs of Search Engines | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳孟彰,盧信銘,蔡銘峰 | |
| dc.subject.keyword | 文字探勘,電影票房預測,查詢記錄分析, | zh_TW |
| dc.subject.keyword | Text Mining,Box office Forecast,Query Log Analysis, | en |
| dc.relation.page | 37 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2011-08-15 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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