We focus on the task of supervised sentiment classification of short and informal texts in Croatian, using two simple yet effective methods: word embeddings and string kernels. We investigate whether word embeddings offer any advantage over corpus-and preprocessing-free string kernels, and how these compare to bag-ofwords baselines. We conduct a comparison on three different datasets, using different preprocessing methods and kernel functions. Results show that, on two out of three datasets,

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... d embeddings outperform string kernels, which in turn outperform word and n-gram bag-of-words baselines.