Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective orientation (polarity) of words such as positive or negative. There are few dictionaries of affective orientation for Spanish; also, the size of these dictionaries is small. Thus, we propose a method for building a large affective Spanish dictionary for subjectivity and sentiment analysis. Supervised learning techniques are used to classify the entries from a lexical dictionary according to their affective orientations based on their definitions. We combine three classifiers (decision trees, naive Bayes, and a support vector machine) to determine the final polarity of each entry, that is, positive or negative. [For the complete volume, "New Perspectives on Teaching and Working with Languages in the Digital Era," see ED565799.]