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. In this paper, 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. Finally, we provide managerial implications for firms and help them make proper strategies based on the experiment results.