  While researchers often study message features like moral content in text,
such as party manifestos and social media, their quantification remains a
challenge. Conventional human coding struggles with scalability and intercoder
reliability. While dictionary-based methods are cost-effective and
computationally efficient, they often lack contextual sensitivity and are
limited by the vocabularies developed for the original applications. In this
paper, we present an approach to construct vec-tionary measurement tools that
boost validated dictionaries with word embeddings through nonlinear
optimization. By harnessing semantic relationships encoded by embeddings,
vec-tionaries improve the measurement of message features from text, especially
those in short format, by expanding the applicability of original vocabularies
to other contexts. Importantly, a vec-tionary can produce additional metrics to
capture the valence and ambivalence of a message feature beyond its strength in
texts. Using moral content in tweets as a case study, we illustrate the steps
to construct the moral foundations vec-tionary, showcasing its ability to
process texts missed by conventional dictionaries and word embedding methods
and to produce measurements better aligned with crowdsourced human assessments.
Furthermore, additional metrics from the vec-tionary unveiled unique insights
that facilitated predicting outcomes such as message retransmission.
