Since the last century, user experience has been regarded as a key concept in the process of product and service design. With the development of positive psychology, the transformation from negative to positive user experience has also taken place in the field of user experience; it emphasizes exploring the future possibility of positive user experience rather than just solving existing problems. Based on the research and analysis of existing literature, this study makes it clear that positive user experience research should be based on the “positive experience,” and arousing a positive emotion is conducive to improving positive user experience. On this basis, the product emotion theory is applied to the analysis process of “positive experience.” Through word frequency screening, thematic analysis, and correlation calculation, the relationship between product stimulus (object, activity, and identity) and user concern (goal, attitude, and standard) based on positive “user comments” is constructed, and positive user experience is understood from multiple levels. Based on the comment score, the positive user experience interval is divided in order to clarify the improvement direction. Finally, taking the “Angel Orange” unmanned retail terminal as an example, this study carried out an empirical analysis. As an exploratory study, this study can provide some insights into the quantitative research process of positive user experience design that evokes positive emotions from a user’s “positive experience” story.

Along with the rapid application of new information technologies, the data-driven era is coming, and online consumption platforms are booming. However, massive user data have not been fully developed for design value, and the application of data-driven methods of requirement engineering needs to be further expanded. This study proposes a data-driven expectation prediction framework based on social exchange theory, which analyzes user expectations in the consumption process, and predicts improvement plans to assist designers make better design improvement. According to the classification and concept definition of social exchange resources, consumption exchange elements were divided into seven categories: money, commodity, services, information, value, emotion, and status, and based on these categories, two data-driven methods, namely, word frequency statistics and scale surveys, were combined to analyze user-generated data. Then, a mathematical expectation formula was used to expand user expectation prediction. Moreover, by calculating mathematical expectation, explicit and implicit expectations are distinguished to derive a reliable design improvement plan. To validate its feasibility and advantages, an illustrative example of CoCo Fresh Tea & Juice service system improvement design is further adopted. As an exploratory study, it is hoped that this study provides useful insights into the data mining process of consumption comment.