Adjective-based design is a method that translates human perception into design parameters quantitatively in order to achieve better understanding between designers and clients. In this approach, adjectives are used to describe product designs, which are generated via design parameters in terms of geometry. As a requirement of the concept, relations between hull adjectives (e.g., comfortable and aesthetic) and design parameters (e.g., length and width) are learned via a machine-learning algorithm. Nevertheless, the relations cannot be represented by some of the design parameters, although they are in the learning process. This issue shows that the parameters do not impact the adjective choices but add noises to the learning process. Therefore, in this study, visual evaluations are made using eye tracking technology for screening the parameters based on their attractiveness and establishing relations between the attractive ones and the adjectives to enhance quality of the relation representations. Eye tracking is used in perceptual research, which proves the existence of correlations between gaze data and human preferences. The main advantage of eye tracking is that reliable human perception data can more likely be collected compared to the user tests, since the evaluation is based on subjects' attention rather than applying solely questionnaires that are limited by the question content. In light of the benefits and finding, an eye tracking device is used to collect gaze data, and then, eye tracking tools such as Area of Interest (AOI), scan path, and heat map are used to evaluate attractiveness of the design parameters. Finally, regression analysis is used to represent relations between gaze data of design parameters and the adjectives.