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系統識別號 U0026-2906201517192300
論文名稱(中文) 以線上產品評論探勘法建構高涉入性3C產品特徵價格分析模型之研究
論文名稱(英文) Hedonic Analysis for High-involvement Consumer Electronics Using Online Product Reviews
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 103
學期 2
出版年 104
研究生(中文) 李俊文
研究生(英文) Chun-Wen Li
學號 R76021011
學位類別 碩士
語文別 英文
論文頁數 55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-鄭亦君
中文關鍵字 特徵價格分析  產品涉入  意見探勘  情感分析  網路口碑 
英文關鍵字 hedonic analysis  product involvement  opinion mining  sentiment analysis  word-of-mouth 
學科別分類
中文摘要 近年來,線上商品評論已被視為能協助消費者制定購買決策的一種極具價值的資訊來源。過去探討線上商品評論影響力的學術文章,多使用製造商無法控制的因素作為其研究中迴歸模型的解釋變數,例如產品評論的數量、評論的平均評分…等。然而,這些因素卻無法提供直接的管理意涵予廠商參考使用,例如管理者無法輕易地增加商品評論的數量以提升其售價或需求。相反地,他們必須追溯為何評論會成長的原因。因此,為了提供製造商更為直覺的建議,本研究採用特徵價格分析法的概念,其將產品需求分解成多個特徵以辨明哪些特徵影響需求最鉅。再者,先前的研究亦發現當消費者購買高涉入性產品時,在確定購買前,會花費較多的時間與精力搜尋商品資訊,同時在整個搜尋過程中會瀏覽多個外部的評論網站。本研究將智慧型手機市場作為研究標的並提出一研究架構以證實上述的研究假設。
我們的研究架構將利用意見探勘技術從線上商品評論中擷取出情感字詞與商品特徵,並同時使用所擷取出的文字及從產品規格表中所得到的基本商品特徵以構成特徵價格迴歸式。為了更進一步檢驗商品評論的影響力,我們將線上商品評論的來源分成兩類,一種為零售商掌管的網站,而另一種為第三方掌管的網站,並將此差異納入我們的模型內。因此,我們將建構三種不同的迴歸模型以檢測哪一個模型擁有最大的判定係數,其模型分別為僅考慮基本的商品特徵、考慮從零售商掌管的評論來源所擷取的資訊與基本的商品特徵以及考慮從第三方掌管的評論來源所擷取的資訊與基本的商品特徵。最後,我們將基於實驗所得之結果提供管理意涵予企業,以協助他們能制定合適的決策。
英文摘要 In recent years, online product reviews have been considered as a valuable source of information to assist people in making buying decisions. Most of prior studies on the effect of online product reviews have utilized the factors which manufactures cannot control by themselves, such as the number of reviews, the average review rating, as independent variables in their regression models. However, those factors cannot provide direct implications for manufacturers. For example, managers cannot easily increase the number of reviews to rise the product price or demand. In contrast, they have to trace the causes of why the amount of reviews grows. Thus, in order to offer more straightforward suggestions, we adopt the concept of hedonic analysis which decomposes the demand of a commodity into several product features to identify which of them impact its demand mostly. Further, previous surveys find that consumers spend time and effort conducting pre-purchase searches for high-involvement products and visit several external review websites during the search process. In this article, we take smartphone market as our research target and propose a framework to demonstrate these assumptions.
Our framework utilizes opinion mining techniques to extract sentiment words and features from online product reviews, and combines those extracted items with basic characteristics obtained from a specification of each product to form hedonic regressions. In order to examine influences of reviews in more detail, we separate the sources of product reviews into two groups—a retailer-hosted website and third-party hosted websites and take this differential into our models. Thus, we construct three different regression models—(1) considering basic characteristics only; (2) considering the information extracted from a retailer-hosted review source and basic characteristics; (3) considering the information extracted from third-party review sources and basic characteristics, to test which one has the highest value of the coefficient of determination. Finally, we provide managerial implications for firms and help them make proper strategies based on the experiment results.
論文目次 摘 要 I
ABSTRACT II
誌 謝 IV
CONTENTS V
List of Tables VII
List of Figures VIII
Chapter 1 Introduction 1
1.1 Research Background and Motivations 1
1.2 Research Objectives 3
1.3 Research Process 4
Chapter 2 Literature Review 5
2.1 Hedonic Analysis 5
2.2 Sentiment Analysis 6
2.2.1 Sentiment Lexicon Expansion and Aspect Extraction 9
2.3 The Impact of Online Product Reviews 10
2.3.1 Product Involvement 11
2.3.2 Third-party Product Reviews 14
Chapter 3 Research Methods 16
3.1 Data Collection 17
3.2 Opinion Lexicon Expansion and Feature Extraction 18
3.2.1 Dependency Grammar 19
3.2.2 Data Preprocessing 19
3.2.3 Initial Lexicon 20
3.2.4 Propagation Rules 20
3.2.5 Propagation Algorithm 22
3.2.6 Feature Reduction 25
3.2.7 Polarity Assignment 25
3.3 Hedonic Analysis Method 29
3.3.1 Regression Model 31
Chapter 4 Experiment Results 34
4.1 Data 34
4.2 Variables 35
4.2.1 Original Version 35
4.2.2 Expert Version 36
4.2.3 Customer Version 38
4.3 Results 40
4.3.1 Original Version 40
4.3.2 Expert Version 41
4.3.3 Customer Version 43
4.3.4 Comparison 45
Chapter 5 Conclusion 47
5.1 Managerial Implications 47
5.2 Research Limitations 48
5.3 Future Works 49
REFERENCE 51
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