Recently, as customers purchase various products on online stores, they are actively posting their opinions and reviews on the websites of the online stores. The research is being actively conducted to reflect the customer’s demand , which are presented in online customer reviews, in product design. However, the online customer reviews are large in size and consisted of text data, so that it is difficult to elicit engineering specifications from them for product design. Thus, in this study, design attributes affecting customers’ satisfaction were identified and their significance were evaluated through text analytics and statistical analysis. For this, the text data were collected from customer reviews for front loading washers, which are presented in Bestbuy.com. Keywords were extracted through frequency analysis of words appeared in the customers’ reviews and their relationships were analyzed through semantic network analysis. As a result, 7 design attributes affecting customers’ satisfaction were identified through factor analysis and their significance was evaluated through linear regression analysis.