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系統識別號 U0026-1306201410500300
論文名稱(中文) 商品評論特徵探勘模式於銷售量因果關係之研究-以電影社群媒體為例
論文名稱(英文) A Causality Analysis of Sales by Mining Characteristics of Online Reviews- A Case Study in Movie Social Media
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 102
學期 2
出版年 103
研究生(中文) 張有彤
研究生(英文) Yu-Tung Chang
學號 R76014080
學位類別 碩士
語文別 英文
論文頁數 51頁
口試委員 指導教授-李昇暾
口試委員-林清河
口試委員-耿伯文
口試委員-郭淑靜
中文關鍵字 評論特徵  銷售預測  文字探勘  Granger因果關係 
英文關鍵字 Characteristic of review  sales predict  text mining  Granger causality 
學科別分類
中文摘要 發掘消費者購買產品的背後動機一直是廠商感興趣的議題,特別是那些需要花費大量成本投資的商品,商品的成功與否都會為廠商帶來很大的影響。過去的研究指出由消費者分享的評論中隱藏著與銷售量之間的資訊,然而線上評論的資料量隨著時間迅速成長,其中參雜著新舊資訊與正反面意見等,對於潛在消費者與廠商而言越來越難找到想要的資訊。
線上評論中包含評論者給產品的評分、大家給此評論的評分、評論內容的資訊完整程度、評論者的撰寫風格、情緒等等,這些都能影響一篇評論的品質,廠商就需要從線上評論的這些特徵中找出哪些是影響銷售量的關鍵因素。過去的研究大多使用迴歸分析來探討評論特徵與銷售量的關係,而迴歸分析是探討變數間相關程度,並不能確切說明這些特徵與銷售量間有相互影響的關係。
因此本研究提出一個因果關係預測模型,來分析線上評論特徵與銷售量之間的關係。其中為了克服龐大的資訊量我們使用文字探勘及資訊擷取技術,另外在因果關係模型中使用Granger因果關係來檢視各特徵與銷售量的關係。以期達到較前人更好的效果,並能幫助廠商找到線上評論特徵中影響銷售量的關鍵指標。
英文摘要 Manufacturers have always been interested in the reasons or motivations behind product purchase, especially products that involve great investment or have high expectation to succeed. Previous studies have demonstrated that online reviews can be considered a form of electronic word-of-mouth, and provide interesting information that relate to sales and marketing strategies.
However, due to the exponential growth in the number of reviews, companies and customers are finding it increasingly difficult to find desired information. The quality of a review is determined by several characteristics, e.g. helpfulness, rating, writing style, reviewers’ emotion, etc., so it is crucial for manufacturers to find the key characteristics that have a significant effect on sales. Most studies in the past only used regression analysis for examining important characteristics of product reviews. However, correlation does not imply causation, meaning that when two variables are correlated, one does not necessarily cause the other.
For the abovementioned reasons, this study proposes an approach based on the cause and effect model for comprehensively evaluating characteristics of product reviews. To alleviate the issue of information overload, opinion mining and retrieval techniques have been used to extract and retrieve complete characteristics from reviews. Such characteristics will be indicative of the manufacturer’s sales revenue. Results of this study will allow manufacturers understand which indicators to track and manage in order to achieve higher sales.
論文目次 摘要 I
Abstract II
誌謝 III
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Research Objective 2
1.3 The Process of the Research 4
Chapter 2 Literature Review 5
2.1 Online Reviews and Marketing Strategies 5
2.1.1 Marketing Application 5
2.1.2 Impact of Online Reviews to Product Sales 7
2.1.3 Sales Predict by Online Reviews 9
2.2 Characteristics of Online Reviews 12
2.2.1 Individual Reviews Data and Reviewer Characteristics 12
2.2.2 Textual Analysis 13
2.2.3 LIWC 14
2.2.4 Emotional Identification 16
2.3 Granger Causality Test 17
Chapter 3 Research Method 19
3.1 Data Preprocessing 21
3.2 Individual Review Data and Reviewer Characteristics Extraction 21
3.3 Textual Analysis 22
3.3.1 Spelling Errors 22
3.3.2 Evidentiality 22
3.3.3 Flesch Reading Ease 23
3.3.4 Subjectivity 24
3.3.5 LIWC Factor 25
3.3.6 Information Completeness 27
3.4 Emotion Classification 28
3.5 Cause and Effect 29
3.5.1 Unit Root Test 29
3.5.2 Vector Autoregressive Model 29
3.5.3 Granger Causality Test 30
3.5.4 Regression Analysis 30
Chapter 4 Experiment and Analysis 32
4.1 Data collection 32
4.2 Granger Causality Test 33
4.3 Correlation and Prediction Analysis 36
4.3.1 Correlation with Sales by Day 36
4.3.2 Correlation with Sales by Week 36
4.3.3 Prediction with Sales by Day 37
4.3.4 Prediction with Sales by Week 38
4.3.5 Modeling Evaluation 38
4.4 Movie Category 40
4.5 Compare Cause and Effect Model with Other Predictive Models 41
Chapter 5 Conclusion and Future work 45
5.1 Limitation 46
5.2 Future Work 46
References 47
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