跳到主要內容

臺灣博碩士論文加值系統

(44.200.140.218) 您好!臺灣時間:2024/07/18 04:00
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:莊穎融
研究生(外文):CHUANG, YING-JUNG
論文名稱:確認影響電影群眾募資專案之關鍵成功因素
論文名稱(外文):Identifying the Key Successful Factors of Movie Crowdfunding Projects
指導教授:陳隆昇陳隆昇引用關係
指導教授(外文):CHEN, LONG-SHENG
口試委員:陳穆臻陳隆昇蕭宇翔張景榮
口試委員(外文):CHEN, MU-CHENCHEN, LONG-SHENGHSIAO, YU-HSIANGCHANG, JING-RONG
口試日期:2020-07-24
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:117
中文關鍵詞:群眾募資特徵選取決策樹最小絕對收縮和選擇算子倒傳遞神經網路績效評估
外文關鍵詞:CrowdfundingFeature SelectionDecision treeLeast Absolute Shrinkage and Selection OperatorBack Propagation NetworkSupport Vector Machine
相關次數:
  • 被引用被引用:1
  • 點閱點閱:255
  • 評分評分:
  • 下載下載:7
  • 收藏至我的研究室書目清單書目收藏:0
群眾募資(Crowdfunding)已成為許多新創企業獲得資金的重要管道之一。但低成功率一直是一個關鍵問題。因此,如何提高籌款專案的成功率是所有籌款活動的主要關注之一。研究指出專案描述會影響群眾募資專案的成功,專案描述的內容將影響支持者的決定,但仍未有研究深入探討專案描述文字應包含哪些要素,會直接影響專案成功。此外,有研究指出專案參與者的評論情緒會影響群眾募資專案的成功。根據近年群眾募資研究,分別在音樂領域、體育領域等研究對專案募資成功有積極的關鍵因素,但尚未針對電影做探討。因此本研究將研究於電影群眾募資專案的成功影響因素,以專案描述文字與參與募資者的評論為主要研究主體,取代以往文獻使用於問卷調查做為資料收集的來源。因社群評論的影響力日增,所以我們將使用資料探勘和文字探勘方法來分析收集的專案文字描述與評論文字資料,希望改善問卷調查的抽樣誤差的問題。本研究將利用特徵選取方法包含決策樹(Decision tree, DT)、最小絕對收縮和選擇算子(Least Absolute Shrinkage and Selection Operator, LASSO)和倒傳遞神經網路(Back Propagation Neural Network, BPN)來找到影響電影群眾募資專案成功的重要因素,並使用支持向量機(Support Vector Machine, SVM)來評估候選之特徵子集,預期實驗結果可幫助籌款者提高電影類群眾募資專案的成功率。
Crowdfunding has become one of the important channels for many startups to obtain funds. But low success rates have always been a key issue. Therefore, how to improve the success rate of fundraising projects is one of the main concerns of all fundraising activities. The study pointed out that the project description will affect the success of the crowdfunding project, and the content of the project description will affect the supporter's decision. There has been no research to discuss in depth what elements should be included in the project description text, which will directly affect the success of the project. In addition, some studies have pointed out that the sentiment of project participants' comments will affect the success of crowdfunding projects. According to the research on crowdfunding in recent years, studies in the music and sports fields have positive and critical factors for the success of project fundraising, but have not yet discussed the film. Therefore, this study will study the influencing factors of the success of the film crowdfunding project, using the project description and the comments of the fundraisers as the main research subjects, replacing the previous literature used in the questionnaire survey as the source of data collection. Due to the increasing influence of community comments, we will use data exploration and text exploration methods to analyze the collected project text description and comment text data, hoping to improve the sampling error of the questionnaire survey. This study will use feature selection methods including Decision Tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO) and Back Propagation Neural Network (BPN) to find Important factors that affect the success of film crowdfunding projects, and use Support Vector Machine (SVM) to evaluate the candidate feature subset. The expected experimental results can help fundraisers improve the success rate of film crowdfunding projects.
摘要 I
Abstract III
目錄 V
表目錄 VII
圖目錄 XI
第一章 緒 論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 7
第二章 文獻探討 8
2.1 群眾募資專案成功因素 8
2.2 電影對群眾募資專案的潛在影響因素 11
2.3 特徵選取 13
2.4 文字探勘 15
第三章 研究方法 18
3.1 研究流程 18
第四章 實驗結果與分析 30
4.1 定義潛在成功因素 30
4.2 收集資料 34
4.3 特徵選取 36
4.3.1 決策樹(DT) 37
4.3.2倒傳遞神經網路(BPN) 43
4.3.3最小絕對收縮和選擇算子(LASSO) 53
4.4 績效評估(SVM) 59
4.5 小結 65
第五章討論與結論 68
5.1 討論 68
5.2 結論 74
5.3 未來研究方向 75
參考文獻 76
附錄(詞庫) 82
表2-1 群眾募資之相關研究文獻 10
表2-2電影成功之因素文獻整理表 13
表3-1特徵詞庫 20
表3-2詞彙文獻矩陣之範例 23
表3-3混淆矩陣 28
表4-1 KICKSTARTER網站成功率統計表 31
表4-2 KICKSTARTER電影類別成功率募資統計 31
表4-3影響電影群眾募資之重要因素定義 32
表4-4影響電影群眾募資之潛在因素定義 33
表4-5收集資料之敘述統計 35
表4-6 DT在KICKSTARTER平台之專案特徵中重要特徵因素的結果 37
表4-7 DT在KICKSTARTER平台之專案描述和用戶評論中重要特徵因素的結果 38
表4-8 DT在KICKSTARTER平台之全部特徵資料集中重要特徵因素結果 39
表4-9 DT在INDIEGOGO平台之專案特徵中重要特徵因素的結果 40
表4-10 DT在INDIEGOGO平台之專案描述和用戶評論中重要特徵因素的結果 41
表4-11 DT在INDIEGOGO平台之全部特徵資料集中重要特徵因素結果 42
表4-12 BPN在KICKSTARTER平台之專案特徵中因素絕對乘法值 43
表4-13 BPN在KICKSTARTER平台之專案特徵中重要特徵因素的結果 43
表4-14 BPN在KICKSTARTER平台之專案描述和用戶評論中因素絕對乘法值 44
表4-15 BPN在KICKSTARTER平台之專案描述和用戶評論重要特徵因素結果 45
表4-16BPN在KICKSTARTER平台之全部特徵資料集中因素絕對乘法值 46
表4-17 BPN在KICKSTARTER平台之全部特徵資料集重要特徵因素結果 47
表4-18 BPN在INDIEGOGO平台之專案特徵中因素絕對乘法值 48
表4-19BPN在INDIEGOGO平台之專案特徵中重要特徵因素的結果 48
表4-20 BPN在INDIEGOGO平台之專案描述和用戶評論中因素絕對乘法值 49
表4-21 BPN在INDIEGOGO平台之專案描述和用戶評論重要特徵因素結果 50
表4-22 BPN在INDIEGOGO平台之全部特徵資料集中因素絕對乘法值 51
表4-23 BPN在INDIEGOGO平台之全部特徵資料集重要特徵因素結果 52
表4-24 LASSO在KICKSTARTER平台之專案特徵中重要特徵因素結果 53
表4-25 LASSO在KICKSTARTER平台專案描述和用戶評論重要特徵因素結果 54
表4-26 LASSO在KICKSTARTER平台全部特徵料集重要特徵因素結果 55
表4-27 LASSO在INDIEGOGO平台之專案特徵中重要特徵因素結果 56
表4-28 LASSO在INDIEGOGO平台全部特徵資料集重要特徵因素結果 57
表4-29 各方法之特徵選取之顯著因素比較 58
表4-30 SVM評估KICKSTARTER平台之專案特徵之特徵選取績效 59
表4-31SVM評估KICKSTARTER平台之專案描述和用戶評論之特徵選取績效 60
表4-32 SVM評估KICKSTARTER平台之全部特徵資料集之特徵選取績效 61
表4-33 SVM評估INDIEGOGO平台之專案特徵之特徵選取績效 62
表4-34 SVM評估INDIEGOGO平台之專案描述和用戶評論之特徵選取績效 63
表4-35 SVM評估INDIEGOGO平台之全部特徵資料集之特徵選取績效 64
表4-36 DT在INDIEGOGO平台之「專案描述」和「用戶評論」中
重要特徵因素的欠取樣結果 65
表4-37 DT在INDIEGOGO平台之「專案描述」和「用戶評論」中
重要特徵因素的過取樣結果 66
表4-38 SVM評估INDIEGOGO平台之「專案描述」和「用戶評論」之
欠取樣和過取樣的特徵選取績效 67
表5-1 KICKSTARTER平台之重要因素之眾數統計 68
表5-2 INDIEGOGO平台之重要因素之眾數統計 70
表5-3 群眾募資各平台之重要因素統整 72
表5-4 影響電影群眾募資專案成功之重要因素表 73
圖3-1本研究模式階段流程圖 18
圖5-1 KICKSTARTER平台之支持人數分佈長條圖 69
圖5-2 KICKSTARTER平台之募款籌集金額分佈長條圖 69
圖5-3 KICKSTARTER平台之募款目標金額分佈長條圖 70
圖5-4 INDIEGOGO平台之支持人數分佈長條圖 71
圖5-5 INDIEGOGO平台之募款籌集金額分佈長條圖 71
圖5-6 INDIEGOGO平台之募款目標金額分佈長條圖 72


1.Alamsyah, A., Nugroho, T.B.A., 2018, Predictive modelling for startup and investor relationship based on crowdfunding platform data,” IOP Conf. Series: Journal of Physics: Conf. Series 971.
2.Agrawal, A., Catalini, C., Goldfarb, A., 2015, “Crowdfunding: geography, social networks and the timing of investment decisions,” J. Econ. Manag. Strateg, Volume24, Issue2, pp. 253–274.
3.Arena, M., Bengo, I., Calderini, M., Chiodo, V., 2018, “Unlocking finance for social tech start-ups: is there a new opportunity space?,” Technological Forecasting & Social Change 127, pp.154–165.
4.Aleksina, A., Akulenka, S., Lublóy, A., 2019, “Success factors of crowdfunding campaigns in medical research: perceptions and reality,” Volume 24, pp. 1413-1420.
5.Ahlers, G.K.C., Cumming, D., Guenther, C., Schweizer, D., 2015, “Signaling in Equity Crowdfunding,” Entrepreneurship Theory and Practice 39, pp. 955–980.
6.Bagheri, A., Chitsazan, H., Ebrahimi, A., 2019, “Crowdfunding motivations: A focus on donors' perspectives,” Technological Forecasting and Social Change 146, pp. 218-232.
7.Belleflamme, P., Lambert, T., 2014, “Crowdfunding: Some Empirical Findings and Microeconomic Underpinnings,” SSRN Electronic Journal, pp. 288-296.
8.Belleflamme, P., Lambert, T., Schwienbacher, A., 2014, “Crowdfunding: tapping the right crowd,” Journal of Business Venturing, Volume 29, Issue 5, pp. 585–609.
9.Bouncken, R.B., Komorek, M., Kraus, S., 2015, “Crowdfunding: The current state of research,” International Business & Economics Research Journal, Volume 14, No. 3, pp.407-416.
10.Bagheri, A., Chitsazan, H., Ebrahimi, A., 2019, “Crowdfunding motivations: A focus on donors' perspectives,” Technological Forecasting and Social Change, pp. 218-232.
11.Bhave, A., Kulkarni, H., Biramane, V., Kosamkar, P., 2015, “Role of different factors in predicting movie success,” ICPC, pp. 1-4.
12.Berliner, L.S., Kenworthy, N.J., 2017, “Producing a worthy illness: personal crowdfunding amidst financial crisis,” Soc. Sci. Med. 187, pp. 233–242.
13.Bretschneider, U., Leimeister, J.M., 2017, “Not just an ego-trip: Exploring backers’ motivation for funding in incentive-based crowdfunding,” The Journal of Strategic Information Systems, solume 26, Issue 4, pp. 246-260.
14.Cholakova, M., Clarysse, B., 2015, “Does the possibility to make equity investments in crowdfunding projects crowd out reward-based investments?,” Entrepreneurship Theory and Practice, Vol. 39, Issue 1, pp. 145–172.
15.Chouat, O., Irawan, A.H., 2018, “Implementation of Data Mining on Online Shop in Indonesia,” INCITEST, pp.1-7.
16.Colombo, M.G., Butticè, V., Fumagalli, E., Orsenigo, C., 2018, “Green oriented crowdfunding campaigns: their characteristics and diffusion in different institutional settings,” Technological Forecasting & Social Change 141, pp. 85–97.
17.Cumming, D.J., Hornuf, L., Karami, M., Schweizer, D., 2020, “Disentangling Crowdfunding from Fraudfunding,” Max Planck Institute for Innovation & Competition Research Paper No. pp. 9-16.
18.Chitsazan, H., 2019, “Factors Affecting Crowdfunding Success: A Systematic Analysis of the Empirical Studies,” ICCIKE, pp. 20-24.
19.Chan, H.F., Moy, N., Schaffner, M., Torgler, B., 2019, “The effects of money saliency and sustainability orientation on reward based crowdfunding success,” Journal of Business Research 0148-2963, pp. 1-13.
20.Cizmeci, B., Oguducu, S.G., 2018, “Predicting IMDb Ratings of Pre-release Movies with Factorization Machines Using Social Media,” UBMK, pp. 173-178.
21.Du, L., Hu, M., Wu, J., 2017, “Contingent stimulus in crowdfunding. Rotman School of Management,” Rotman School of Management Working Paper, No. 2925962, pp.1-53.
22.Davidson, R., Poor, N., 2014, “The barriers facing artists’ use of crowdfunding platforms: Personality, emotional labor, and going to the well one too many times,” New Media & Society 17, no. 2, pp. 289-307.
23.Dhir, R., Raj, A., 2018, “Movie Success Prediction using Machine Learning Algorithms and their Comparison,” ICSCCC, pp. 385-390.
24.Devi, S.G., Sabrigiriraj, M., 2018, “Feature Selection, Online Feature Selection Techniques for Big Data Classification: -A Review,” Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies 18637371.
25.Forbes, H., Schaefer, D., 2017, “Guidelines for successful crowdfunding,” Procedia CIRP 60, pp. 398–403.
26.Gopinath, S., Chintagunta, P.K., Venkataraman, S., 2013, “Blogs, Advertising, and Local-Market Movie Box Office Performance,” Management Science, vol. 59, no. 12, pp. 2635–2654.
27.Guo, Y., Chung, F.L., Li, G., Zhang, L., 2019, “Multi-Label Bioinformatics Data Classification With Ensemble Embedded Feature Selection,” IEEE Access, Volume 7, pp. 103863-103875.
28.Hearst, M., 2003, “What is text mining?, ” [online] Available.
29.Hardiyanti, N., Lawi, A., Diaraya, Aziz, F., 2018, “Classification of Human Activity based on Sensor Accelerometer and Gyroscope Using Ensemble SVM method,” EIConCIT, pp. 304-307.
30.Hassan, D., 2018, “A Text Mining Approach for Evaluating Event Credibility on Twitter,” WETICE, 1524-4547.
31.Jayasekara, P.K., Abu, K.S., 2018, “Text Mining of Highly Cited Publications in Data Mining,” ETTLIS 52959634.
32.Kraus, S., Richter, C., Brem, A., Cheng, C.F., Chang, M.L., 2016, “Strategies for rewardbased crowdfunding campaigns,” J. Innov. Knowl. Volume 1, No.1, pp. 13–23.
33.Kwon, S., Lee, S., Kim, Y., 2015, “Moderately clipped LASSO,” Computational Statistics & Data Analysis, Volume 92, pp. 53-67.
34.Kim, Y., Lee, J., Lee, E.B., J Lee, J.H., 2020, “Application of Natural Language Processing (NLP) and Text-Mining of Big-Data to Engineering-Procurement-Construction (EPC) Bid and Contract Documents,” CDMA 18637371.
35.Karami, A., Lundy, M., Webb, F., Dwivedi, Y.K., 2020, “Twitter and Research: A Systematic Literature Review Through Text Mining,” IEEE Access, Volume 8, pp. 67698-67717.
36.Liu, H., Motoda, H., 2012, “Feature selection for knowledge discovery and data mining,” Springer Science & Business Media, vol. 454, pp. 214.
37.Liu, H., Motoda, H., 2007, “Computational Methods of Feature Selection,” Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, pp.440.
38.Liao, S., Chu, P., Hsiao, P., 2012, “Review: Data mining techniques and applications - A decade review from 2000 to 2011,” Expert Systems with Applications, Volume 39, Issue 12, pp. 11303-11311.
39.Langley, P., 1994, “Selection of relevant features in machine learning,” AAAI, Volume 97, Issues 1–2, pp. 245-271.
40.Kumar, S.S., Shaikh, T., 2017, “Empirical Evaluation of the Performance of Feature Selection Approaches on Random Forest,” ICCA, vol. 14, No.3, 17287811.
41.Mollick, E., 2014, “The dynamics of crowdfunding: an exploratory study,” J. Bus. Ventur. 29, No.1, pp. 1–16.
42.Mitra, T., Gilbert, E., 2014, “The language that gets people to give: phrases that predict success on Kickstarter,” Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 49–61.
43.Mondal, W.I., 2018, “The More, The Better? Movie Genre and Performance Analysis,” Journal of Business and Educational Leadership Vol 7, No 1, pp. 1-153.
44.Mestyán, M.C.A., Yasseri, T., Kertész, J., 2013, “Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data,” PLoS ONE, vol. 8, No. 8, pp. 1-8.
45.Meyskens, M., Bird, L., 2015, “Crowdfunding and Value Creation,” Crowdfunding and Value Creation, pp. 155–166.
46.Mariani, A., Annunziata, A., Aprile, M.C., Nacchia, F., 2017,“Crowdfunding and wine business: Some insights from Fundovino experience,” Wine Economics and Policy, vol. 6, no. 1, pp. 60-70.
47.Miglo, A., 2020, “Crowdfunding in a Competitive Environment,” Journal of Risk and Financial Management, Technological Forecasting & Social Change 141, pp. 138–148.
48.Novielli, N., Calefato, F., Lanubile, F., 2020, “Love, Joy, Anger, Sadness, Fear, and Surprise: SE Needs Special Kinds of AI: A Case Study on Text Mining and SE,” IEEE Software, Volume 37 , Issue 3, pp. 86-91.
49.Petitjean, M., 2018, “What explains the success of reward-based crowdfunding campaigns as they unfold?,” Evidence from the French crowdfunding platform KissKissBankBank. Financ. Res. Lett. 26, pp. 9–14.
50.Petruzzelli, A.M., Natalicchio, A., Panniello, P., Roma, P., 2019, “Understanding the Crowdfunding Phenomenon and Its Implications for Sustainability,” Technological Forecasting and Social Change 141, pp. 138-148.
51.Qu,Y., Fang, Y., Yan, F., 2019, “Feature Selection Algorithm Based on Association Rules,” Journal of Physics: Conference Series, Volume 1168, Issue 5, pp. 1-8.
52.Quader, N., Gani, M.O., Chaki, D., Ali, M.H., 2017, “A Machine Learning Approach to Predict Movie Box-Office Success,” ICCIT, pp. 1-66.
53.Robertson, E.N., Wooster, R.B., 2015, “Crowdfunding as a social movement: The determinants of success in Kickstarter campaigns,” SSRN Electronic Journal, pp. 1-35.
54.Robert, T., 1996, “Regression Shrinkage and Selection via the lasso,” Journal of the Royal Statistical Society, Vol. 58, No. 1, pp. 267-288.
55.Shruti, Roy, S.D., Zeng, W., 2014, “Influence of Social Media on Performance of Movies,” ICMEW, 14581303.
56.Sinha, A.A., Krishna, S.V.V., Shedge, R., Sinha, A., 2017, “Movie Production Investment Decision System,” ICECDS, pp. 1-10.
57.Simonoff, J.S., Sparrow, I.R., 2000, “Predicting Movie Grosses: Winners and Losers, Blockbusters and Sleepers,” Chance, vol. 13, no. 3, pp. 15–24.
58.Sung, S.F., Lin, C.Y., Hu, Y.H., 2020, “EMR-based phenotyping of ischemic stroke using supervised machine learning and text mining techniques,” IEEE Journal of Biomedical and Health Informatics, Early Access, 32142458.
59.Sallehuddin, R., Rosely, N.F.L.M.R., Zain, A.M., 2019, “Overview Feature Selection using Fish Swarm Algorithm,” IOP Conf. Series: Journal of Physics: Conf., Series 1192012068.
60.Trindade, G., Silva, T.M.T.C., Santos, M.D.C., 2017, “Determinants of the crowdfunding campaign success in the areas of music and sports,” CISTI, 17028987.
61.Teng, X., Gong, Y., 2018, “Research on Application of Machine Learning in Data Mining,” Materials Science and Engineering, Vol. 392, pp. 1-4.
62.Taylor, D.G., Levin, M., 2014, “Predicting Mobile App Usage for Purchasing and Information-Sharing,” International Journal of Retail & Distribution Management, Vol. 42, no. 8, pp.759–774.
63.Verma, G., Verma, H., 2019, “Predicting Bollywood Movies Success Using Machine Learning Technique,” AICAI 139108578, pp. 1-4.
64.Wehnert, P., Baccarella C., Beckmann, M., 2019, “In crowdfunding we trust? Investigating crowdfunding success as a signal for enhancing trust in sustainable product features,” Volume 141, pp. 128-137.
65.Wang, Z., Zhang, J., Ji, S., Meng, C., Li, T., Zheng, Y., 2020, “Predicting and ranking box office revenue of movies based on big data,” Information Fusion Journal, Volume 60, pp. 25-40.
66.Yasen, M., Tedmori, S., 2019, “Movies Reviews Sentiment Analysis and Classification,” JEEIT 18691563, pp. 1-4.
67.Yang, F.J., 2019, “An Extended Idea about Decision Trees,” CSCI.
68.Teng, X., Gong, Y., 2018, “Research on Application of Machine Learning in Data Mining,” Materials Science and Engineering 392, 062202, pp. 1-5.
69.Yuan, H., Lau, R.Y.K., Xu, W., 2016, “The determinants of crowdfunding success: A semantic text analytics approach,” Decision Support Systems 91 pp. 67–76.
70.Zaw, T., Tun, K.M.M., Oo, A.M., 2019, “Price Forecasting by Back Propagation Neural Network Model,” ICAIT, 19222358, pp. 1-5.
71.Zvilichovsky, D., Danziger, S., Steinhart, Y., 2018, “Making-the-Product-Happen: A Driver of Crowdfunding Participation,” Journal of Interactive Marketing, vol. 41, issue C, pp. 81-93.
72.Zhao, J., Zhang, C., Yao, F., 2020, “Efficient Selection of Visual Features in Automatic Image Retrieval,” ICMTMA, 19493690, pp. 1-4.
73.Zhang, H., Chen, W., 2019, “Crowdfunding technological innovations: Interaction between consumer benefits and rewards,” Journal of Technological Innovation, Volumes 84–85, pp. 11-20.
74.Zhang, T., Zhu, T., Xiong, P., Huo, H., Tari, Z., Zhou, W., 2020, “Correlated Differential Privacy: Feature Selection in Machine Learning,” IEEE Transactions on Industrial Informatics, vol. 16, No. 3, pp. 2115-2124.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊