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    <titleInfo>
        <title>Moral Stance Recognition and Polarity Classification from Twitter and Elicited Text</title>
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    <name type="personal">
        <namePart type="given">Wesley</namePart>
        <namePart type="family">Santos</namePart>
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    <name type="personal">
        <namePart type="given">Ivandré</namePart>
        <namePart type="family">Paraboni</namePart>
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        <dateIssued>2019-09</dateIssued>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
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    <abstract>We introduce a labelled corpus of stances about moral issues for the Brazilian Portuguese language, and present reference results for both the stance recognition and polarity classification tasks. The corpus is built from Twitter and further expanded with data elicited through crowd sourcing and labelled by their own authors. Put together, the corpus and reference results are expected to be taken as a baseline for further studies in the field of stance recognition and polarity classification from text.</abstract>
    <identifier type="citekey">santos-paraboni-2019-moral</identifier>
    <identifier type="doi">10.26615/978-954-452-056-4_123</identifier>
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        <url>https://aclanthology.org/R19-1123</url>
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    <part>
        <date>2019-09</date>
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            <start>1069</start>
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%0 Conference Proceedings
%T Moral Stance Recognition and Polarity Classification from Twitter and Elicited Text
%A Santos, Wesley
%A Paraboni, Ivandré
%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 santos-paraboni-2019-moral
%X We introduce a labelled corpus of stances about moral issues for the Brazilian Portuguese language, and present reference results for both the stance recognition and polarity classification tasks. The corpus is built from Twitter and further expanded with data elicited through crowd sourcing and labelled by their own authors. Put together, the corpus and reference results are expected to be taken as a baseline for further studies in the field of stance recognition and polarity classification from text.
%R 10.26615/978-954-452-056-4_123
%U https://aclanthology.org/R19-1123
%U https://doi.org/10.26615/978-954-452-056-4_123
%P 1069-1075

Markdown (Informal)

[Moral Stance Recognition and Polarity Classification from Twitter and Elicited Text](https://aclanthology.org/R19-1123) (Santos & Paraboni, RANLP 2019)

• Moral Stance Recognition and Polarity Classification from Twitter and Elicited Text (Santos & Paraboni, RANLP 2019)

ACL

• Wesley Santos and Ivandré Paraboni. 2019. Moral Stance Recognition and Polarity Classification from Twitter and Elicited Text. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1069–1075, Varna, Bulgaria. INCOMA Ltd..