Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted
  Sentiment Classification Benchmark
Łukasz Augustyniak Szymon Woźniak Marcin Gruza Piotr Gramacki Krzysztof Rajda Mikołaj Morzy Tomasz Kajdanowicz
Despite impressive advancements in multilingual corpora collection and model
training, developing large-scale deployments of multilingual models still
presents a significant challenge. This is particularly true for language tasks
that are culture-dependent. One such example is the area of multilingual
sentiment analysis, where affective markers can be subtle and deeply ensconced
in culture. This work presents the most extensive open massively multilingual
corpus of datasets for training sentiment models. The corpus consists of 79
manually selected datasets from over 350 datasets reported in the scientific
literature based on strict quality criteria. The corpus covers 27 languages
representing 6 language families. Datasets can be queried using several
linguistic and functional features. In addition, we present a multi-faceted
sentiment classification benchmark summarizing hundreds of experiments
conducted on different base models, training objectives, dataset collections,
and fine-tuning strategies.