音樂演唱會的總收益，向來是音樂產業內部進行票價訂定時的標準之一；假設某策展團體預計邀請一位從未造訪該國之音樂人進行演出時，許多人都認為Youtube歌曲瀏覽量能夠幫助他們預測總收益。此外，其網路聲量、演出地點、是否發行新歌等變數出發，對於策展人來說，也是考量市場是否有足夠需求之重要特徵。本研究據以機器學習之支持向量機演算法，配合模糊理論與統計分類方法，將所蒐集到之二次資料進行變數分群，歸納並預測出不同總收益之音樂人的特徵變量為何。
透過蒐集兩年期之Pollstar資料，並建立在演唱會產業之業界觀點（覺醒音樂祭 Wake Up Festival / 傲頭厝展演空間 Our Town Love House），來發想此研究內容。研究結果顯示，據本研究所蒐集之變量特徵來將音樂人族群進行分群，若是音樂人能透過Youtube歌曲瀏覽量及其他特徵變數分類，得知該場演唱會的總收益分群將落在哪個區間，將可符合在地市場之趨勢與需求，便於分析與預測營收，並從加以訂價，從中創造收益。

Concert tickets revenue has always been a component of the pricing mechanism. When selecting among foreign performers, most of the concert promoters consider the views on Youtube to be helpful for building pricing model. Furthermore, to promoters, other factors including internet word of mouth, exhibition location or new-released albums are also crucial attributes regarding the demand in music industry. That’s why this research, based on the SVM model, intend to manifest what are the features that are able to predict the potential revenue.
The study is developed through the combination of 2-year data on Pollstar and the practical point of view from “Wake Up Festival” and “Our Town Love House.” The indications is: the collected features could cluster the performers; phrased otherwise, applying the views on Youtube and other related features, promoters could better conduct the price mechanism and create value since the local market trend and demand are analyzed and well-understood.

目錄
論文審定書............................................................................................................................................................i
誌謝....................................................................................................................................................................... ii
摘要.......................................................................................................................................................................iii
Abstract................................................................................................................................................................iv
目錄.........................................................................................................................................................................v
圖次........................................................................................................................................................................vi
表次.................................................................................................................................................................... viii

第一章 緒論.................................................................................................1
1.1 研究背景................................................................................................1
1.2 研究動機與缺口.....................................................................................2
1.3 研究問題................................................................................................3
第二章 文獻回顧..........................................................................................4
2.1 音樂演唱會產業.....................................................................................4
2.2 篩選特徵變數演算法.............................................................................6
2.3 支持向量機............................................................................................7
第三章 研究方法........................................................................................15
3.1 研究模型介紹......................................................................................15
3.2 資料來源..............................................................................................17
3.2.1 主要資料來源：Pollstar、Kworb....................................................17
3.2.2 變數來源...........................................................................................19
3.2.3 資料清洗...........................................................................................22
3.3 變數介紹..............................................................................................23
3.4 篩選變數演算法..................................................................................25
3.5 LASSO REGRESSION.........................................................................26
3.5.1 L1正則化.........................................................................................26
3.6支持向量機.........................................................................................28
第四章 研究結果......................................................................................29
4.1 資料敘述統計....................................................................................29
4.1.1變數之資料敘述統計......................................................................29
4.2 篩選特徵變數演算法........................................................................36
4.2.1 Lasso Regression.........................................................................36
4.4支持向量機結果................................................................................39
第五章 結論............................................................................................42
5.1 研究結論..........................................................................................42
5.2 研究限制..........................................................................................43
參考文獻................................................................................................44

圖次
圖2-1 Lasso Regression 之L1 正則化.................................................................................................. 7
圖2-2 支持向量機超平面示意圖.............................................................................................................9
圖2-3 支持向量機最大間隔超平面示意圖...........................................................................................10
圖3-1 演唱會特徵分析架構圖...................................................................................................................16
圖3-2 POLLSTAR 官網操作介面..........................................................................................................18
圖3-3 KWORB.NET 官網操作介面......................................................................................................19
圖3-4 LIVE BOXOFFICE DATABASE ..............................................................................................20
圖3-5 GLOBAL CONCERT PULSE .......................................................................................................22
圖3-6 挑選最佳懲罰值(LAMBDA) ..........................................................................................................27
圖4-1 訓練集總收益之資料分佈(取對數) ...........................................................................................34
圖4-2 測試集總收益之資料分佈(取對數) ........................................................................................... 34
圖4-3 訓練集之總收益QQ圖....................................................................................................................35
圖4-4 測試集之總收益QQ圖 .................................................................................................................35

表次
表3-1 POLLSTAR BOXOFFICE提供之變數....................................................................................21
表3-2 變數介紹...............................................................................................................................................23
表3-3 變數因素向量化.................................................................................................................................26
表4-1 場次及日期之敘述統計表 .............................................................................................................30
表4-2 虛擬變數之敘述統計表...................................................................................................................32
表4-3 分群資料之敘述統計表...................................................................................................................33
表4-4 LASSO REGRESSION 之RMSE...............................................................................................36
表4-5 LASSO REGRESSION 篩選變數...............................................................................................37
表4-6 LASSO REGRESSION 挑選之子集合.....................................................................................38
表4-7 訓練集之混淆矩陣.............................................................................................................................40
表4-8 測試集之混淆矩陣.............................................................................................................................40
表4-9 訓練集與測試集之分群預測準確度...........................................................................................41
表4-10 精確率、召回率、F1 Score.........................................................................................................41
表5-1 高低收益分群之特徵變數整理....................................................................................................42

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