<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kocon-etal-2019-multi-level">
    <titleInfo>
        <title>Multi-level analysis and recognition of the text sentiment on the example of consumer opinions</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jan</namePart>
        <namePart type="family">Kocoń</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Monika</namePart>
        <namePart type="family">Zaśko-Zielińska</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Piotr</namePart>
        <namePart type="family">Miłkowski</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-sep</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
        </titleInfo>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this article, we present a novel multi-domain dataset of Polish text reviews, annotated with sentiment on different levels: sentences and the whole documents. The annotation was made by linguists in a 2+1 scheme (with inter-annotator agreement analysis). We present a preliminary approach to the classification of labelled data using logistic regression, bidirectional long short-term memory recurrent neural networks (BiLSTM) and bidirectional encoder representations from transformers (BERT).</abstract>
    <identifier type="citekey">kocon-etal-2019-multi-level</identifier>
    <identifier type="doi">10.26615/978-954-452-056-4_066</identifier>
    <location>
        <url>https://aclanthology.org/R19-1066</url>
    </location>
    <part>
        <date>2019-sep</date>
        <extent unit="page">
            <start>559</start>
            <end>567</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Multi-level analysis and recognition of the text sentiment on the example of consumer opinions
%A Kocoń, Jan
%A Zaśko-Zielińska, Monika
%A Miłkowski, Piotr
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 sep
%I INCOMA Ltd.
%C Varna, Bulgaria
%F kocon-etal-2019-multi-level
%X In this article, we present a novel multi-domain dataset of Polish text reviews, annotated with sentiment on different levels: sentences and the whole documents. The annotation was made by linguists in a 2+1 scheme (with inter-annotator agreement analysis). We present a preliminary approach to the classification of labelled data using logistic regression, bidirectional long short-term memory recurrent neural networks (BiLSTM) and bidirectional encoder representations from transformers (BERT).
%R 10.26615/978-954-452-056-4_066
%U https://aclanthology.org/R19-1066
%U https://doi.org/10.26615/978-954-452-056-4_066
%P 559-567

Markdown (Informal)

[Multi-level analysis and recognition of the text sentiment on the example of consumer opinions](https://aclanthology.org/R19-1066) (Kocoń et al., RANLP 2019)

• Multi-level analysis and recognition of the text sentiment on the example of consumer opinions (Kocoń et al., RANLP 2019)

ACL

• Jan Kocoń, Monika Zaśko-Zielińska, and Piotr Miłkowski. 2019. Multi-level analysis and recognition of the text sentiment on the example of consumer opinions. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 559–567, Varna, Bulgaria. INCOMA Ltd..