  Informal first-person narratives are a unique resource for computational
models of everyday events and people's affective reactions to them. People
blogging about their day tend not to explicitly say I am happy. Instead they
describe situations from which other humans can readily infer their affective
reactions. However current sentiment dictionaries are missing much of the
information needed to make similar inferences. We build on recent work that
models affect in terms of lexical predicate functions and affect on the
predicate's arguments. We present a method to learn proxies for these functions
from first-person narratives. We construct a novel fine-grained test set, and
show that the patterns we learn improve our ability to predict first-person
affective reactions to everyday events, from a Stanford sentiment baseline of
.67F to .75F.
