In the product design field, it is important to pin point critical product form features (PFFs) that influence consumers' affective responses (CARs) of a product design. In this paper, an approach based on least squares support vector regression (LS-SVR) and automatic relevance determination (ARD) is proposed to streamline the task of product form feature selection (PFFS) according to the CAR data. The representation of PFFs is determined by morphological analysis and pairwise adjectives are used to express CARs. In order to gather the CAR data, an experiment of semantic differential (SD) evaluation on collected product samples was conducted. The LS-SVR prediction model can be constructed using the PFFs as input data and the evaluated SD scores as output value. The optimal parameters of the LS-SVR model are tuned by using Bayesian inference. Finally, an ARD selection process is used to analyze the relative relevance of PFFs to obtain feature ranking.