In general, blogs have simple texts, which are of easy assimilation; however, due to the large number of blogs in recent years, it is often difficult to choose one. This research studies the use of the sentiment and affective analysis in a recommendation system (RS) of blogs through texts extracted from a social network. Some blogs of different themes are selected and previously classified according to the polarity of their texts. A recommendation of blogs is carried out, according to the relationship between the sentiment and affective analysis of both, the blog content and the texts posted by users. Results show that the use of sentiment and affective analysis improved the RS performance reaching 89% of users' acceptance, against to 55% when sentiment and affective analysis is not considered. Also, the system interface implemented in a mobile device is evaluated considering an ergonomic criteria set.