| 研究生: |
陳昭瑜 Chen, Chao-Yu |
|---|---|
| 論文名稱: |
以資料探勘方法預測群眾募資綠色專案之成敗 Success Prediction for crowdfunding green projects by data mining methods |
| 指導教授: |
施勵行
Shih, Li-Hshih |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 資源工程學系 Department of Resources Engineering |
| 論文出版年: | 2018 |
| 畢業學年度: | 106 |
| 語文別: | 中文 |
| 論文頁數: | 102 |
| 中文關鍵詞: | 資料探勘 、永續群眾募資 、影響募資成功的因素 、主題模型 、集群分析 、決策樹 |
| 外文關鍵詞: | Sustainable, crowdfunding, Latent Dirichlet Allocation, cluster analysis, decision tree |
| 相關次數: | 點閱:76 下載:0 |
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群眾募資是現在創新者籌募資金的管道,而募資專案的方向和目的會影響募資的成敗。本研究針對綠色創新的募資專案的成功與否進行研究,希望能藉由群眾募資平台上現有的資料,以資料探勘的方式建立這些資料與募資成功機率之間的決策樹模型,並預測模型的準確度。
根據以往的文獻指出,影響募資成功的因素包括:募資專案類別、目標金額、募資天數、是否有宣傳影片或圖片、回饋方案、專案進度報告次數、提案者與投資者的互動次數、提案者過去的提案經驗、社群媒體分享次數、贊助人數等。
本研究是以文字探勘中的主題模型來找出群眾募資專案描述之主中心思想與理念,並根據主題模型結果對應聯合國永續發展目標(SDGs),將群眾募資專案主題分為永續與非永續後,再將群眾募資專案以集群分析分為產品類別與服務類別,最後將數值資料與文字資料整合進行決策樹模型分析,找出群眾募資專案成功與否的規則,以及評估決策樹模型的預測能力。
本研究希望透過資料探勘的方式了解永續或非永續、產品類別或服務類別之群眾募資專案之成功與否之規則,希望能幫助未來的提案者能藉由募資平台上所提供的資訊來判定募資專案成功的可能性。
SUMMARY
Since Financial crisis of 2008, due to small and medium enterprises to borrow is not easy, crowdfunding had become very popular to very popular between start-up companies and personal studio. Currently, regarding with sustainable issue all around the world, many international convention and policies had pass in The United Nations Conference on Environment and Development, such as The Sustainable Development Goals (SDGs), etc. No only to legislate, countries in the world also made effort to sustainable development. People in the world can not only abide by the law, but take actions on doing the right things, for instance, we can propose ideas on crowdfunding platform, and use the platform to raise funds.
Therefore, in this study, we are going to use Latent Dirichlet Allocation (LDA) which is one of the method in text mining to find out the crowdfunding projects’ central idea, each of the central idea is one of the topic models. Base on the result of LDA topic modle we contrast with The Sustainable Development Goals (SDGs) of 17 global goals to devided the projects into sustainable and non-sustainable. Then we use cluster analysis to make the projects groups. Finaly, we combind the numerical data and text data and use CART decision tree model to find if-then rules and evaluate the result.
Key words: Sustainable, crowdfunding, Latent Dirichlet Allocation, cluster analysis, decision tree
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