User-generated texts such as reviews, comments or discussions are valuable indicators of users’ preferences. Unlike previous works which focus on labeled data from user-contributed reviews, we focus here on user comments which are not accompanied by pre-defined rating labels. We investigate their role in a one-class collaborative filtering task such as bookmarking, where only the user action is given as ground-truth. We propose a sentiment-aware nearest neighbor model (SANN) for multimedia recommendations over TED talks, which makes use of user comments. The model outperforms significantly (by more than 25% on unseen data) several competitive baselines.