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    <titleInfo>
        <title>Enhancing General Sentiment Lexicons for Domain-Specific Use</title>
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    <name type="personal">
        <namePart type="given">Tim</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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        <name type="personal">
            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
            <namePart type="family">Isabelle</namePart>
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    <abstract>Lexicon based methods for sentiment analysis rely on high quality polarity lexicons. In recent years, automatic methods for inducing lexicons have increased the viability of lexicon based methods for polarity classification. SentProp is a framework for inducing domain-specific polarities from word embeddings. We elaborate on SentProp by evaluating its use for enhancing DuOMan, a general-purpose lexicon, for use in the political domain. By adding only top sentiment bearing words from the vocabulary and applying small polarity shifts in the general-purpose lexicon, we increase accuracy in an in-domain classification task. The enhanced lexicon performs worse than the original lexicon in an out-domain task, showing that the words we added and the polarity shifts we applied are domain-specific and do not translate well to an out-domain setting.</abstract>
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        <url>https://aclanthology.org/C18-1090</url>
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%0 Conference Proceedings
%T Enhancing General Sentiment Lexicons for Domain-Specific Use
%A Kreutz, Tim
%A Daelemans, Walter
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F kreutz-daelemans-2018-enhancing
%X Lexicon based methods for sentiment analysis rely on high quality polarity lexicons. In recent years, automatic methods for inducing lexicons have increased the viability of lexicon based methods for polarity classification. SentProp is a framework for inducing domain-specific polarities from word embeddings. We elaborate on SentProp by evaluating its use for enhancing DuOMan, a general-purpose lexicon, for use in the political domain. By adding only top sentiment bearing words from the vocabulary and applying small polarity shifts in the general-purpose lexicon, we increase accuracy in an in-domain classification task. The enhanced lexicon performs worse than the original lexicon in an out-domain task, showing that the words we added and the polarity shifts we applied are domain-specific and do not translate well to an out-domain setting.
%U https://aclanthology.org/C18-1090
%P 1056-1064

Markdown (Informal)

[Enhancing General Sentiment Lexicons for Domain-Specific Use](https://aclanthology.org/C18-1090) (Kreutz & Daelemans, COLING 2018)

• Enhancing General Sentiment Lexicons for Domain-Specific Use (Kreutz & Daelemans, COLING 2018)

ACL

• Tim Kreutz and Walter Daelemans. 2018. Enhancing General Sentiment Lexicons for Domain-Specific Use. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1056–1064, Santa Fe, New Mexico, USA. Association for Computational Linguistics.