Comparison of Short-Text Sentiment Analysis Methods for Croatian

Abstract

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

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Reproductions