  Sentic computing relies on well-defined affective models of different
complexity - polarity to distinguish positive and negative sentiment, for
example, or more nuanced models to capture expressions of human emotions. When
used to measure communication success, even the most granular affective model
combined with sophisticated machine learning approaches may not fully capture
an organisation's strategic positioning goals. Such goals often deviate from
the assumptions of standardised affective models. While certain emotions such
as Joy and Trust typically represent desirable brand associations, specific
communication goals formulated by marketing professionals often go beyond such
standard dimensions. For instance, the brand manager of a television show may
consider fear or sadness to be desired emotions for its audience. This article
introduces expansion techniques for affective models, combining common and
commonsense knowledge available in knowledge graphs with language models and
affective reasoning, improving coverage and consistency as well as supporting
domain-specific interpretations of emotions. An extensive evaluation compares
the performance of different expansion techniques: (i) a quantitative
evaluation based on the revisited Hourglass of Emotions model to assess
performance on complex models that cover multiple affective categories, using
manually compiled gold standard data, and (ii) a qualitative evaluation of a
domain-specific affective model for television programme brands. The results of
these evaluations demonstrate that the introduced techniques support a variety
of embeddings and pre-trained models. The paper concludes with a discussion on
applying this approach to other scenarios where affective model resources are
scarce.
