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    <titleInfo>
        <title>Sentiment analysis under temporal shift</title>
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            <title>Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
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        <name type="personal">
            <namePart type="given">Alexandra</namePart>
            <namePart type="family">Balahur</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Saif</namePart>
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            <namePart type="given">Veronique</namePart>
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    <abstract>Sentiment analysis models often rely on training data that is several years old. In this paper, we show that lexical features change polarity over time, leading to degrading performance. This effect is particularly strong in sparse models relying only on highly predictive features. Using predictive feature selection, we are able to significantly improve the accuracy of such models over time.</abstract>
    <identifier type="citekey">lukes-sogaard-2018-sentiment</identifier>
    <identifier type="doi">10.18653/v1/W18-6210</identifier>
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%0 Conference Proceedings
%T Sentiment analysis under temporal shift
%A Lukes, Jan
%A Søgaard, Anders
%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 lukes-sogaard-2018-sentiment
%X Sentiment analysis models often rely on training data that is several years old. In this paper, we show that lexical features change polarity over time, leading to degrading performance. This effect is particularly strong in sparse models relying only on highly predictive features. Using predictive feature selection, we are able to significantly improve the accuracy of such models over time.
%R 10.18653/v1/W18-6210
%U https://aclanthology.org/W18-6210/
%U https://doi.org/10.18653/v1/W18-6210
%P 65-71

Markdown (Informal)

[Sentiment analysis under temporal shift](https://aclanthology.org/W18-6210/) (Lukes & Søgaard, WASSA 2018)

• Sentiment analysis under temporal shift (Lukes & Søgaard, WASSA 2018)

ACL

• Jan Lukes and Anders Søgaard. 2018. Sentiment analysis under temporal shift. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 65–71, Brussels, Belgium. Association for Computational Linguistics.