The aim of this poster is to investigate the role of emotion in the collaborative filtering task. For this purpose, a kernel-based collaborative recommendation technique is used. The experiment is conducted on two MovieLens data sets. The emotional features are extracted from the movie reviews and plot summaries. The results show that emotional features are capable of enhancing recommendation effectiveness