  A word's sentiment depends on the domain in which it is used. Computational
social science research thus requires sentiment lexicons that are specific to
the domains being studied. We combine domain-specific word embeddings with a
label propagation framework to induce accurate domain-specific sentiment
lexicons using small sets of seed words, achieving state-of-the-art performance
competitive with approaches that rely on hand-curated resources. Using our
framework we perform two large-scale empirical studies to quantify the extent
to which sentiment varies across time and between communities. We induce and
release historical sentiment lexicons for 150 years of English and
community-specific sentiment lexicons for 250 online communities from the
social media forum Reddit. The historical lexicons show that more than 5% of
sentiment-bearing (non-neutral) English words completely switched polarity
during the last 150 years, and the community-specific lexicons highlight how
sentiment varies drastically between different communities.
