  We present a generalized paradigm for adaptation of propositional analysis
(predicate-argument pairs) to new tasks and domains. We leverage an analogy
between stances (belief-driven sentiment) and concerns (topical issues with
moral dimensions/endorsements) to produce an explanatory representation. A key
contribution is the combination of semi-automatic resource building for
extraction of domain-dependent concern types (with 2-4 hours of human labor per
domain) and an entirely automatic procedure for extraction of
domain-independent moral dimensions and endorsement values. Prudent (automatic)
selection of terms from propositional structures for lexical expansion (via
semantic similarity) produces new moral dimension lexicons at three levels of
granularity beyond a strong baseline lexicon. We develop a ground truth (GT)
based on expert annotators and compare our concern detection output to GT, to
yield 231% improvement in recall over baseline, with only a 10% loss in
precision. F1 yields 66% improvement over baseline and 97.8% of human
performance. Our lexically based approach yields large savings over approaches
that employ costly human labor and model building. We provide to the community
a newly expanded moral dimension/value lexicon, annotation guidelines, and GT.
