  In natural language the intended meaning of a word or phrase is often
implicit and depends on the context. In this work, we propose a simple yet
effective method for sentiment analysis using contextual embeddings and a
self-attention mechanism. The experimental results for three languages,
including morphologically rich Polish and German, show that our model is
comparable to or even outperforms state-of-the-art models. In all cases the
superiority of models leveraging contextual embeddings is demonstrated.
Finally, this work is intended as a step towards introducing a universal,
multilingual sentiment classifier.
