<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kaljahi-foster-2018-sentiment">
    <titleInfo>
        <title>Sentiment Expression Boundaries in Sentiment Polarity Classification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Rasoul</namePart>
        <namePart type="family">Kaljahi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jennifer</namePart>
        <namePart type="family">Foster</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Alexandra</namePart>
            <namePart type="family">Balahur</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Saif</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Mohammad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Veronique</namePart>
            <namePart type="family">Hoste</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Roman</namePart>
            <namePart type="family">Klinger</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Brussels, Belgium</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We investigate the effect of using sentiment expression boundaries in predicting sentiment polarity in aspect-level sentiment analysis. We manually annotate a freely available English sentiment polarity dataset with these boundaries and carry out a series of experiments which demonstrate that high quality sentiment expressions can boost the performance of polarity classification. Our experiments with neural architectures also show that CNN networks outperform LSTMs on this task and dataset.</abstract>
    <identifier type="citekey">kaljahi-foster-2018-sentiment</identifier>
    <identifier type="doi">10.18653/v1/W18-6222</identifier>
    <location>
        <url>https://aclanthology.org/W18-6222</url>
    </location>
    <part>
        <date>2018-10</date>
        <extent unit="page">
            <start>156</start>
            <end>166</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Sentiment Expression Boundaries in Sentiment Polarity Classification
%A Kaljahi, Rasoul
%A Foster, Jennifer
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kaljahi-foster-2018-sentiment
%X We investigate the effect of using sentiment expression boundaries in predicting sentiment polarity in aspect-level sentiment analysis. We manually annotate a freely available English sentiment polarity dataset with these boundaries and carry out a series of experiments which demonstrate that high quality sentiment expressions can boost the performance of polarity classification. Our experiments with neural architectures also show that CNN networks outperform LSTMs on this task and dataset.
%R 10.18653/v1/W18-6222
%U https://aclanthology.org/W18-6222
%U https://doi.org/10.18653/v1/W18-6222
%P 156-166

Markdown (Informal)

[Sentiment Expression Boundaries in Sentiment Polarity Classification](https://aclanthology.org/W18-6222) (Kaljahi & Foster, WASSA 2018)

• Sentiment Expression Boundaries in Sentiment Polarity Classification (Kaljahi & Foster, WASSA 2018)

ACL

• Rasoul Kaljahi and Jennifer Foster. 2018. Sentiment Expression Boundaries in Sentiment Polarity Classification. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 156–166, Brussels, Belgium. Association for Computational Linguistics.