What is the size of their published dataset?
911
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.
Sentiment analysis has become an established field with a number of subfields (aspect-level sentiment analysis, social media sentiment analysis, cross-lingual sentiment analysis), all of which require some kind of annotated resource, either to train a machine-learning based classifier or to test the performance of proposed approaches. Although much research into multi-lingual and cross-lingual sentiment analysis has focused on unsupervised or semi-supervised approaches BIBREF0 , BIBREF1 , BIBREF2 , these techniques still require certain resources (linked wordnets, seed lexicon) and do not generally reach the performance of supervised approaches. In English the state-of-the-art for binary sentiment analysis often reaches nearly 90 percent accuracy BIBREF3 , BIBREF4 , BIBREF5 , but for other languages there is a marked drop in accuracy. This is mainly due to the lack of annotations and resources in these languages. This is especially true of corpora annotated at aspect-level. Unlike document- or tweet-level annotation, aspect-level annotation requires a large amount of effort from the annotators, which further reduces the likelihood of finding an aspect-level sentiment corpus in under-resourced languages. We are, however, aware of one corpus annotated for aspects in German BIBREF6 , although German is not a particularly low-resource language. The movement towards multi-lingual datasets for sentiment analysis is important because many languages offer different challenges, such as complex morphology or highly productive word formation, which can not be overcome by focusing only on English data. The novelty of this work lies in creating corpora which cover both Basque and Catalan languages and are annotated in such a way that they are compatible with similarly compiled corpora available in a number of languages BIBREF7 . This allows for further research into cross-lingual sentiment analysis, as well as introducing the first resource for aspect-level sentiment analysis in Catalan and Basque. The corpus is available at http://hdl.handle.net/10230/33928 or https://jbarnesspain.github.io/resources/.