  In this paper, we introduce a new Czech subjectivity dataset of 10k manually
annotated subjective and objective sentences from movie reviews and
descriptions. Our prime motivation is to provide a reliable dataset that can be
used with the existing English dataset as a benchmark to test the ability of
pre-trained multilingual models to transfer knowledge between Czech and English
and vice versa. Two annotators annotated the dataset reaching 0.83 of the
Cohen's \k{appa} inter-annotator agreement. To the best of our knowledge, this
is the first subjectivity dataset for the Czech language. We also created an
additional dataset that consists of 200k automatically labeled sentences. Both
datasets are freely available for research purposes. Furthermore, we fine-tune
five pre-trained BERT-like models to set a monolingual baseline for the new
dataset and we achieve 93.56% of accuracy. We fine-tune models on the existing
English dataset for which we obtained results that are on par with the current
state-of-the-art results. Finally, we perform zero-shot cross-lingual
subjectivity classification between Czech and English to verify the usability
of our dataset as the cross-lingual benchmark. We compare and discuss the
cross-lingual and monolingual results and the ability of multilingual models to
transfer knowledge between languages.
