  We propose new computational models for analyzing self-reported emotional
diary texts of pregnant women to support maternal care. We gathered affective
ratings outside clinical setting and developed new models to facilitate
interpretation and communication of affective expressions between persons
representing different affective ratings. Relying on constructed emotion
theory, models of dimensional emotion categories and affective ratings of Self
Assessment Manikin, we demonstrate our new proposal to analyze linguistic data
with computational models exploiting vector space and clustering methods. 35
persons having Finnish as a native language provided affective ratings for 195
emotional adjectives and 16 pregnancy-related nouns in Finnish in dimensions of
pleasure, arousal and dominance. We developed new models to represent
dependencies and differences of affective ratings between various population
subgroup categorizations, including "women without children", "women with
children" and "men without children" that we consider important population
segments to be addressed in maternal care. Our affective ratings showed
significant correlations between pleasure and dominance (like Warriner et al.,
2013) and with previous data collections (S\"oderholm et al., 2013; Eilola &
Havelka, 2010; Warriner et al., 2013). Our affective ratings had significant
effects on categorizations based on gender, gender-parental role and the time
of the day and duration of giving ratings. Our results indicate accordance with
significant affectivity differences of gender and age (Warriner et al., 2013)
and motherhood (Rosebrock et al., 2015). Our proposed models aim to support
health-related communication. Our results suggest gathering next the affective
ratings of patients of maternal care in a real clinical setting.
