  This paper presents a novel approach for multi-lingual sentiment
classification in short texts. This is a challenging task as the amount of
training data in languages other than English is very limited. Previously
proposed multi-lingual approaches typically require to establish a
correspondence to English for which powerful classifiers are already available.
In contrast, our method does not require such supervision. We leverage large
amounts of weakly-supervised data in various languages to train a multi-layer
convolutional network and demonstrate the importance of using pre-training of
such networks. We thoroughly evaluate our approach on various multi-lingual
datasets, including the recent SemEval-2016 sentiment prediction benchmark
(Task 4), where we achieved state-of-the-art performance. We also compare the
performance of our model trained individually for each language to a variant
trained for all languages at once. We show that the latter model reaches
slightly worse - but still acceptable - performance when compared to the single
language model, while benefiting from better generalization properties across
languages.
