  While sentiment analysis has become an established field in the NLP
community, research into languages other than English has been hindered by the
lack of resources. Although much research in multi-lingual and cross-lingual
sentiment analysis has focused on unsupervised or semi-supervised approaches,
these still require a large number of resources and do not reach the
performance of supervised approaches. With this in mind, we introduce two
datasets for supervised aspect-level sentiment analysis in Basque and Catalan,
both of which are under-resourced languages. We provide high-quality
annotations and benchmarks with the hope that they will be useful to the
growing community of researchers working on these languages.
