Abstract

Within a digital corpus of 20 Italian post-unification conduct books, UAM CorpusTool is used to perform a manual annotation of 13 emotive rhetorical figures as indices of “shown” emotion. The analysis consists in two text mining tasks: classification, which identifies emotive figures using the 13 categories, and clustering, which identifies groups, i.e. clusters where emotive figures co-occur. Emotive clusters mainly discuss diligence and parsimony—personal values linked to self-improvement for which reader agreement is not taken for granted. In this corpus they function as “moving” values, that is, values acting recurrently as contexts for “argued” emotion.