  In this paper, we explore sentiment composition in phrases that have at least
one positive and at least one negative word---phrases like 'happy accident' and
'best winter break'. We compiled a dataset of such opposing polarity phrases
and manually annotated them with real-valued scores of sentiment association.
Using this dataset, we analyze the linguistic patterns present in opposing
polarity phrases. Finally, we apply several unsupervised and supervised
techniques of sentiment composition to determine their efficacy on this
dataset. Our best system, which incorporates information from the phrase's
constituents, their parts of speech, their sentiment association scores, and
their embedding vectors, obtains an accuracy of over 80% on the opposing
polarity phrases.

Computation and Language