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        <title>A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts</title>
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        <namePart type="given">Fei</namePart>
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            <title>Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)</title>
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        <name type="personal">
            <namePart type="given">Malvina</namePart>
            <namePart type="family">Nissim</namePart>
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        <name type="personal">
            <namePart type="given">Viviana</namePart>
            <namePart type="family">Patti</namePart>
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        <name type="personal">
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    <abstract>Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits compared with prior work. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.</abstract>
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%0 Conference Proceedings
%T A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts
%A Liu, Fei
%A Perez, Julien
%A Nowson, Scott
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%S Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F liu-etal-2016-recurrent
%X Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits compared with prior work. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.
%U https://aclanthology.org/W16-4303
%P 20-29

Markdown (Informal)

[A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts](https://aclanthology.org/W16-4303) (Liu et al., PEOPLES 2016)

• A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts (Liu et al., PEOPLES 2016)

ACL

• Fei Liu, Julien Perez, and Scott Nowson. 2016. A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 20–29, Osaka, Japan. The COLING 2016 Organizing Committee.