The tradition of dimensional models in the study of emotions suggests that affective space is better defined by a small number of non-specific general dimensions. This dimensional perspective in the study of emotions postulates that the minimal entities of representation are dimensions such as valence (attraction vs. rejection) and arousal (level of activation). Data collection in this perspective is done through the laborious process of resorting to the estimates of hundreds of people who must decide on a continuum of two dimensions: how positive or negative the object to which the concept alludes is, and what level of arousal it generates. To do so, generally, an image-based questionnaire developed to measure an emotional response is used, called SAM (Self Assessment Manikin), which is a set of synthesized drawings that can be used to guide the participants' response. As can be noted, the volume and quality of the procedures used to study these affective variables associated with concepts involves an arduous process of data collection and processing. The complexity of this work, due to the enormous amount and type of data, makes computational intelligence a very useful and novel tool for its approach in order to obtain reliable and reproducible results. Processing by means of distributional semantics obtains the meaning of a word by locating the context in which it appears through an intelligent search in large volumes of electronically stored data. Lexical-affective information, represented by the valence and activation dimensions, is a type of information that seems to be represented by the social use of a term and, therefore, it is plausible to infer it by studying the contexts in which the term appears. In this paper we consider the plausibility of the application of the method of estimating lexical-affective values by means of distributional semantics for the Spanish language. In order to achieve this objective, the Spanish adaptation of the ANEW is used for this purpose and, two procedures were carried out. On the one hand, the most traditionally used computational method of estimation by means of linear regressions was compared with a model based on neural networks, which showed the better fit of the latter. On the other hand, lexical-affective values were estimated for a set of complex emotions and, in order to verify the strength of the results, such estimation was compared with empirical data taken and processed in a linguistic community (Argentine), different from the community that gave rise to the data with which the computational model was trained (Spanish). The results have been very encouraging since, between the computationally derived estimates and the empirically derived data with the Argentine population, correlations were found to be sufficiently strong and comparable to those that can be found when the same comparison is made between empirically derived results with different age groups, or between different genders, or when comparing different geographical regions. These results point to a striking common base of the language, a basic common core of concepts, which can be explored by means of the procedures and techniques of distributional semantics. [ABSTRACT FROM AUTHOR]