博碩士論文 etd-0626119-132258 詳細資訊


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姓名 李劭竑(Shao-Hung Lee) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Department of Information Management)
畢業學位 碩士(Master) 畢業時期 107學年第2學期
論文名稱(中) 直播訂閱預測─以Twitch平台為例
論文名稱(英) A study of prediction of Live-stream subscriptions: the case of Twitch
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    紙本論文:3 年後公開 (2022-07-26 公開)

    電子論文:使用者自訂權限:校內 2 年後、校外 2 年後公開

    論文語文/頁數 英文/46
    統計 本論文已被瀏覽 5510 次,被下載 0 次
    摘要(中) 近年來,出現了一種新型媒體“直播”,透過串流的技術,直播可以讓人們向他人展示自己的生活,並在低延遲的環境下與他人即時的互動,這種新型態的媒體極大的改善了傳播者和受眾之間的用戶體驗。台灣有著許多直播平台,其中以Twitch最為著名,在2018年,全球每天平均有1500萬人觀看Twtich上的直播,而這些人會帶來龐大的商機。
    因此,在本研究中,我們欲探索Twitch平台上的實況主和觀眾之間的關係,目的是預測觀眾是否願意成為訂閱者而付費給平台與實況主。本研究使用Twitch API和網絡爬蟲來收集數據,利用文本挖掘來捕捉觀眾在直播聊天室中的行為,並標記普通觀眾和訂閱者。接著我們使用一些監督式學習的方法,如邏輯式回歸,SVM,決策樹和隨機森林,來構建模型進行預測。
    最終模型的準確度可達到0.7366,而資料蒐集的區間越短,準確率會越高。
    觀看者在聊天室裡留言的頻率、是否開啟直播的通知、觀看者的追隨直播主的數量以及觀看者在聊天室留言的負面情緒對於訂閱直播主有著顯著影響。
    摘要(英) In recent years, there has been a new type of media, “live stream”. Through streaming technology, it can make people show their lives to others and interact with others at low latency. This kind of media greatly improve the user experience between communicators and audience. Among many live stream platforms, Twitch is most famous in Taiwan. In 2018, there were an average of 15 million people watching live stream on Twtich every day . A large number of people will bring a lot of business opportunities.
    Therefore, in this study, we want to explore the relationship between the live streamers and viewers on the Twitch platform. We want to predict whether viewers will be willing to pay for subscriptions. This study uses Twitch API and web crawler to collect data. We use text mining to catch viewers’ actions in live stream’s chat room and label general viewers and subscribers. And then, we use some supervised learning methods, such as logistic regression, SVM, decision tree and random forest, to build models to predict.
    The best model’s accuracy could reach 0.73664. And the result indicates that the chat frequency, live stream’s notification and the number of viewer’s following influence whether the viewer will subscribe the streamer.
    關鍵字(中)
  • 監督式學習
  • 直播
  • 文本挖掘
  • 訂閱
  • 社交媒體
  • 關鍵字(英)
  • Supervised learning
  • Text mining
  • Subscription
  • Live stream
  • Social media
  • 論文目次 論文審定書 i
    論文公開授權書 ii
    中文摘要 iii
    Abstract iv
    Chapter 1. Introduction 1
    1.1 Research Background 1
    1.2 Research Motivation 3
    1.3 Research Purpose 6
    Chapter 2. Literature Review 7
    2.1 Live streaming 7
    2.2 Live chatting 7
    2.3 The categories of viewers on Twitch 8
    2.4 Donation/Subscription intention 9
    2.5 Text mining 10
    2.6 Classification method 12
    Chapter 3. Method 17
    3.1 Data Collecting 17
    3.2 Data Preprocessing 19
    3.3 Data Splitting 21
    3.4 Data Mining 21
    Chapter 4. Result 23
    4.1 Descriptive statistics 23
    4.2 Model performance 24
    4.2.1 Original models 24
    4.2.2 Stepwise models 25
    4.2.3 Committee machine 26
    4.2.4 Logistic coefficient analysis 27
    4.3 Discussion 27
    Chapter 5. Conclusion 29
    5.1 Implications for research 29
    5.2 Implications for practice 29
    5.3 Limitations and future research 30
    References 31
    Appendix 37
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    口試委員
  • 李彥賢 - 召集委員
  • 江成欣 - 委員
  • 楊淯程 - 指導教授
  • 口試日期 2019-07-05 繳交日期 2019-07-26

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