Title Page
Abstract
Contents
Chapter 1. Introduction 7
Chapter 2. Literature Review 9
1.1. Hedonic Pricing 9
1.2. Pricing Subscription Services 11
1.3. Hedonic Pricing on Subscription Services 13
1.4. Data Mining through Machine Learning 13
1.5. Research questions derived and organized table of literatures 15
Chapter 3. Development Process and Methodologies 17
3.1. Hedonic Pricing Model 19
3.2. Data Collection & Preprocessing 20
3.2.1. Data 20
3.2.2. Data scraping & Preprocessing 21
3.2.3. LDA, TF-IDF 22
3.2.4. Obtain regression model by PLS (Supervised LDA) 25
3.2.5. Define values of attributes & Hedonic regression model 28
Chapter 4. Application 29
4.1. Pricing each OTT service with LDA and PLS 29
4.2. Results and Implications 33
4.2.1. Implications regarding customer review-based pricing 35
4.2.2. Insights from the prices and attributes carried out from the process of pricing 5 OTT subscription services 36
4.2.3. Implications regarding methodologies 38
Chapter 5. Conclusion 39
Bibliography 41
Appendices 46
Appendix A. Sample of TF-IDF result conducted for the application part 46
Appendix B. Result of coefficients brought by Y prediction and dimensionality reduction with PLS algorithm conducted for the application part 47
Appendix C. Result of trials on getting values of independent variables using OLS (Ordinary Least Squares) regression 48
Appendix D. Trials on testing coefficients to reduce dimensionality with other methods, LASSO regression. 49
요약 (국문초록) 50