  The categorization of emotion names, i.e., the grouping of emotion words that
have similar emotional connotations together, is a key tool of Social
Psychology used to explore people's knowledge about emotions. Without
exception, the studies following that research line were based on the gauging
of the perceived similarity between emotion names by the participants of the
experiments. Here we propose and examine a new approach to study the categories
of emotion names - the similarities between target emotion names are obtained
by comparing the contexts in which they appear in texts retrieved from the
World Wide Web. This comparison does not account for any explicit semantic
information; it simply counts the number of common words or lexical items used
in the contexts. This procedure allows us to write the entries of the
similarity matrix as dot products in a linear vector space of contexts. The
properties of this matrix were then explored using Multidimensional Scaling
Analysis and Hierarchical Clustering. Our main findings, namely, the underlying
dimension of the emotion space and the categories of emotion names, were
consistent with those based on people's judgments of emotion names
similarities.
