Online reviews have a significant impact on the decisions of consumers, providing valuable information which must be managed from two different perspectives: that of the user who reads the review and the people who gave those opinions. These two perspectives are the basis of the novel fuzzy aspect-based sentiment analysis approach described in this paper to recommend the most suitable products for a specific user. This approach consists of a T1OWA-based mechanism to characterize the user profile, which is able to model whether the user can be more influenced by negative opinions or positive opinions, a mechanism for determining their preferences, and a variation coefficient method for weighting the importance of the aspects of the product reviews. Combining these ideas, our model outperforms other well-known methods for ranking products, while also having the advantage of being adaptable to the preferences and characteristics of a specific user.