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    <titleInfo>
        <title>Predicting Sentiment of Polish Language Short Texts</title>
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        <namePart type="given">Aleksander</namePart>
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    <name type="personal">
        <namePart type="given">Julita</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>The goal of this paper is to use all available Polish language data sets to seek the best possible performance in supervised sentiment analysis of short texts. We use text collections with labelled sentiment such as tweets, movie reviews and a sentiment treebank, in three comparison modes. In the first, we examine the performance of models trained and tested on the same text collection using standard cross-validation (in-domain). In the second we train models on all available data except the given test collection, which we use for testing (one vs rest cross-domain). In the third, we train a model on one data set and apply it to another one (one vs one cross-domain). We compare wide range of methods including machine learning on bag-of-words representation, bidirectional recurrent neural networks as well as the most recent pre-trained architectures ELMO and BERT. We formulate conclusions as to cross-domain and in-domain performance of each method. Unsurprisingly, BERT turned out to be a strong performer, especially in the cross-domain setting. What is surprising however, is solid performance of the relatively simple multinomial Naive Bayes classifier, which performed equally well as BERT on several data sets.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_151</identifier>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Predicting Sentiment of Polish Language Short Texts
%A Wawer, Aleksander
%A Sobiczewska, Julita
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F wawer-sobiczewska-2019-predicting
%X The goal of this paper is to use all available Polish language data sets to seek the best possible performance in supervised sentiment analysis of short texts. We use text collections with labelled sentiment such as tweets, movie reviews and a sentiment treebank, in three comparison modes. In the first, we examine the performance of models trained and tested on the same text collection using standard cross-validation (in-domain). In the second we train models on all available data except the given test collection, which we use for testing (one vs rest cross-domain). In the third, we train a model on one data set and apply it to another one (one vs one cross-domain). We compare wide range of methods including machine learning on bag-of-words representation, bidirectional recurrent neural networks as well as the most recent pre-trained architectures ELMO and BERT. We formulate conclusions as to cross-domain and in-domain performance of each method. Unsurprisingly, BERT turned out to be a strong performer, especially in the cross-domain setting. What is surprising however, is solid performance of the relatively simple multinomial Naive Bayes classifier, which performed equally well as BERT on several data sets.
%R 10.26615/978-954-452-056-4_151
%U https://aclanthology.org/R19-1151
%U https://doi.org/10.26615/978-954-452-056-4_151
%P 1321-1327

Markdown (Informal)

[Predicting Sentiment of Polish Language Short Texts](https://aclanthology.org/R19-1151) (Wawer & Sobiczewska, RANLP 2019)

• Predicting Sentiment of Polish Language Short Texts (Wawer & Sobiczewska, RANLP 2019)

ACL

• Aleksander Wawer and Julita Sobiczewska. 2019. Predicting Sentiment of Polish Language Short Texts. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1321–1327, Varna, Bulgaria. INCOMA Ltd..