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        <title>Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering</title>
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        <namePart type="given">João</namePart>
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    <name type="personal">
        <namePart type="given">Lyle</namePart>
        <namePart type="family">Ungar</namePart>
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            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
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            <namePart type="given">Phil</namePart>
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    <abstract>Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains. We develop a method that automatically extends word-level ratings to unrated words using signed clustering of vector space word representations along with affect ratings. We use our method to determine a word‘s valence and arousal, which determine its position on the circumplex model of affect, the most popular dimensional model of emotion. Our method achieves superior out-of-sample word rating prediction on both affective dimensions across three different languages when compared to state-of-the-art word similarity based methods. Our method can assist building word ratings for new languages and improve downstream tasks such as sentiment analysis and emotion detection.</abstract>
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%0 Conference Proceedings
%T Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering
%A Sedoc, João
%A Preoţiuc-Pietro, Daniel
%A Ungar, Lyle
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F sedoc-etal-2017-predicting
%X Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains. We develop a method that automatically extends word-level ratings to unrated words using signed clustering of vector space word representations along with affect ratings. We use our method to determine a word‘s valence and arousal, which determine its position on the circumplex model of affect, the most popular dimensional model of emotion. Our method achieves superior out-of-sample word rating prediction on both affective dimensions across three different languages when compared to state-of-the-art word similarity based methods. Our method can assist building word ratings for new languages and improve downstream tasks such as sentiment analysis and emotion detection.
%U https://aclanthology.org/E17-2090/
%P 564-571

Markdown (Informal)

[Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering](https://aclanthology.org/E17-2090/) (Sedoc et al., EACL 2017)

• Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering (Sedoc et al., EACL 2017)

ACL

• João Sedoc, Daniel Preoţiuc-Pietro, and Lyle Ungar. 2017. Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 564–571, Valencia, Spain. Association for Computational Linguistics.