電影票房總是與口碑不一致？建構口碑與電影票房之分類模式

Constructing the U.S. movie classification model

10.6338/JDA.201806_13(3).0001

李永新(Yung Hsin Lee)；陳怡妃(I-Fei Chen)；鄔欣佑(Sin-You Wu)

鑑別分析 ； 多元適應性雲形迴歸 ； 分類回歸樹 ； 票房 ； multivariate adaptive regression splines (MARS) ； box office forecasting ； classification and regression tree(CART) ； linear discriminant analysis

Journal of Data Analysis

13卷3期（2018 / 06 / 01）

1 - 24

繁體中文

在科技的進步下，近年來電影產業發展快速，票房在全球逐年突破新紀錄，而市場上任何一部電影上映之後，最終以「高口碑高票房」、「高口碑低票房」、「低口碑高票房」、「低口碑低票房」，這四種結局找到歸宿，過去研究中沒有一個分類標準來區分這四種結局，故本研究利用電影相關特性來做為區分票房與口碑之重要變數，並利用鑑別分析(LDA)、多元適應性雲形迴歸(MARS)、分類回歸樹(CART)等分類方法，探討影響分類的決定因素以及其影響程度。結果顯示，本研究使用多元適應性雲形迴歸，在分類準確率明顯優於另外兩種分類方法，CART整體準確率雖較差，但透過CART分類規則，可以找出隱藏在資料中的重要影響變數，可提供電影產業相關業者，利用現有的資源與網路資源，掌握電影票房銷售與口碑可能落點，並規劃應對的行銷方案，創造出更大的收益結果。

Due to advances in technology, the rapidly developing of film industry breakthrough a new record of box office year after year in the global in recent years. After a movie releasing on the market will ultimately to 「High WOM High box office」、「High WOM low box office」、「low WOM High box office」、「low WOM low box office」 these four outcomes as classification. Previous study have no criteria to distinguish these four outcomes before. Therefore, our thesis takes advantage of film characteristics to classify these four types of movies by multivariate adaptive regression splines (MARS), classification and regression tree(CART) and linear discriminant analysis (LDA). The empirical results show that MARS outperformed the other two techniques and also identified the important factors which matter these four movie types.

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