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        <title>Comparison of Short-Text Sentiment Analysis Methods for Croatian</title>
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    <name type="personal">
        <namePart type="given">Leon</namePart>
        <namePart type="family">Rotim</namePart>
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        <namePart type="given">Jan</namePart>
        <namePart type="family">Šnajder</namePart>
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            <title>Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Tomaž</namePart>
            <namePart type="family">Erjavec</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jakub</namePart>
            <namePart type="family">Piskorski</namePart>
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        <name type="personal">
            <namePart type="given">Lidia</namePart>
            <namePart type="family">Pivovarova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Josef</namePart>
            <namePart type="family">Steinberger</namePart>
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        <name type="personal">
            <namePart type="given">Roman</namePart>
            <namePart type="family">Yangarber</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/W17-1411</identifier>
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%0 Conference Proceedings
%T Comparison of Short-Text Sentiment Analysis Methods for Croatian
%A Rotim, Leon
%A Šnajder, Jan
%Y Erjavec, Tomaž
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F rotim-snajder-2017-comparison
%X 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.
%R 10.18653/v1/W17-1411
%U https://aclanthology.org/W17-1411
%U https://doi.org/10.18653/v1/W17-1411
%P 69-75

Markdown (Informal)

[Comparison of Short-Text Sentiment Analysis Methods for Croatian](https://aclanthology.org/W17-1411) (Rotim & Šnajder, BSNLP 2017)

• Comparison of Short-Text Sentiment Analysis Methods for Croatian (Rotim & Šnajder, BSNLP 2017)

ACL

• Leon Rotim and Jan Šnajder. 2017. Comparison of Short-Text Sentiment Analysis Methods for Croatian. In Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing, pages 69–75, Valencia, Spain. Association for Computational Linguistics.