  Sentiment analysis in low-resource languages suffers from a lack of annotated
corpora to estimate high-performing models. Machine translation and bilingual
word embeddings provide some relief through cross-lingual sentiment approaches.
However, they either require large amounts of parallel data or do not
sufficiently capture sentiment information. We introduce Bilingual Sentiment
Embeddings (BLSE), which jointly represent sentiment information in a source
and target language. This model only requires a small bilingual lexicon, a
source-language corpus annotated for sentiment, and monolingual word embeddings
for each language. We perform experiments on three language combinations
(Spanish, Catalan, Basque) for sentence-level cross-lingual sentiment
classification and find that our model significantly outperforms
state-of-the-art methods on four out of six experimental setups, as well as
capturing complementary information to machine translation. Our analysis of the
resulting embedding space provides evidence that it represents sentiment
information in the resource-poor target language without any annotated data in
that language.
