  Sentiment analysis benefits from large, hand-annotated resources in order to
train and test machine learning models, which are often data hungry. While some
languages, e.g., English, have a vast array of these resources, most
under-resourced languages do not, especially for fine-grained sentiment tasks,
such as aspect-level or targeted sentiment analysis. To improve this situation,
we propose a cross-lingual approach to sentiment analysis that is applicable to
under-resourced languages and takes into account target-level information. This
model incorporates sentiment information into bilingual distributional
representations, by jointly optimizing them for semantics and sentiment,
showing state-of-the-art performance at sentence-level when combined with
machine translation. The adaptation to targeted sentiment analysis on multiple
domains shows that our model outperforms other projection-based bilingual
embedding methods on binary targeted sentiment tasks. Our analysis on ten
languages demonstrates that the amount of unlabeled monolingual data has
surprisingly little effect on the sentiment results. As expected, the choice of
annotated source language for projection to a target leads to better results
for source-target language pairs which are similar. Therefore, our results
suggest that more efforts should be spent on the creation of resources for less
similar languages to those which are resource-rich already. Finally, a domain
mismatch leads to a decreased performance. This suggests resources in any
language should ideally cover varieties of domains.
