Replication Data for: Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Content

While researchers often study message featureslike 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 toolsthat boost validated dictionaries with word embedding through nonlinear optimization. By harnessing semantic relationships encoded by embeddings, vec-tionariesimprove the measurement of message features from text, especially those in short format, by expanding the applicability of originalvocabularies to other contexts. Importantly, avec-tionary can produce additional metrics tocapture the valence and ambivalence of amessage feature beyond its strength in texts. Using moral content in tweets as acase study,we illustrate the steps to construct the moral foundations vec-tionary, showcasing itsability 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.