The use of online, user-generated content for consumer preference modeling has been a recent topic of interest among the engineering and marketing communities. With the rapid growth of many different types of user-generate content sources, the tasks of reliable opinion extraction and data interpretation are critical challenges. This research investigates one of the largest and most-active content sources, Twitter, and its viability as a content source for preference modeling. Support Vector Machine (SVM) is used for sentiment classification of the messages, and a Twitter query strategy is developed to categorize messages according to product attributes and attribute levels. Over 7,000 messages are collected for a smartphone design case study. The preference modeling results are compared with those from a typical product review study, including over 2,500 product reviews. Overall, the results demonstrate that consumers do express their product opinions through Twitter; thus, this content source could potentially facilitate product design and decision-making via preference modeling.