  Nowadays, there are many applications of text mining over corpora from
different languages. However, most of them are based on texts in prose, lacking
applications that work with poetry texts. An example of an application of text
mining in poetry is the usage of features derived from their individual words
in order to capture the lexical, sublexical and interlexical meaning, and infer
the General Affective Meaning (GAM) of the text. However, even though this
proposal has been proved as useful for poetry in some languages, there is a
lack of studies for both Spanish poetry and for highly-structured poetic
compositions such as sonnets. This article presents a study over an annotated
corpus of Spanish sonnets, in order to analyse if it is possible to build
features from their individual words for predicting their GAM. The purpose of
this is to model sonnets at an affective level. The article also analyses the
relationship between the GAM of the sonnets and the content itself. For this,
we consider the content from a psychological perspective, identifying with tags
when a sonnet is related to a specific term. Then, we study how GAM changes
according to each of those psychological terms.
  The corpus used contains 274 Spanish sonnets from authors of different
centuries, from 15th to 19th. This corpus was annotated by different domain
experts. The experts annotated the poems with affective and lexico-semantic
features, as well as with domain concepts that belong to psychology. Thanks to
this, the corpus of sonnets can be used in different applications, such as
poetry recommender systems, personality text mining studies of the authors, or
the usage of poetry for therapeutic purposes.
