  We investigate cross-lingual sentiment analysis, which has attracted
significant attention due to its applications in various areas including market
research, politics and social sciences. In particular, we introduce a sentiment
analysis framework in multi-label setting as it obeys Plutchik wheel of
emotions. We introduce a novel dynamic weighting method that balances the
contribution from each class during training, unlike previous static weighting
methods that assign non-changing weights based on their class frequency.
Moreover, we adapt the focal loss that favors harder instances from
single-label object recognition literature to our multi-label setting.
Furthermore, we derive a method to choose optimal class-specific thresholds
that maximize the macro-f1 score in linear time complexity. Through an
extensive set of experiments, we show that our method obtains the
state-of-the-art performance in 7 of 9 metrics in 3 different languages using a
single model compared to the common baselines and the best-performing methods
in the SemEval competition. We publicly share our code for our model, which can
perform sentiment analysis in 100 languages, to facilitate further research.
