Over the past several years, social networking services or micro-blogs have become ubiquitously accessible anytime and contain users' opinions expressed in the form of short text messages. In this paper, we introduce a new automatic approach named FEmoRec for emotional context recognition from online social networks that applies a semantic similarity measure based on Multi-Layer Perceptron Neural Net Model. We rely on the assumption that a tweet may belong to many emotional categories with different membership degrees. We classify the tweet by computing an emotion vector that represents the tweet's fuzzy membership values to Ekman's emotion classes. Carried out experiments emphasize the relevance of our proposal, compared to other methods.