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        <title>You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis</title>
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    <name type="personal">
        <namePart type="given">Josip</namePart>
        <namePart type="family">Jukić</namePart>
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    <name type="personal">
        <namePart type="given">Iva</namePart>
        <namePart type="family">Vukojević</namePart>
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    <name type="personal">
        <namePart type="given">Jan</namePart>
        <namePart type="family">Snajder</namePart>
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            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
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        <name type="personal">
            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
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        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
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    <abstract>Expressing attitude or stance toward entities and concepts is an integral part of human behavior and personality. Recently, evaluative language data has become more accessible with social media‘s rapid growth, enabling large-scale opinion analysis. However, surprisingly little research examines the relationship between personality and evaluative language. To bridge this gap, we introduce the notion of evaluative topics, obtained by applying topic models to pre-filtered evaluative text from social media. We then link evaluative topics to individual text authors to build their evaluative profiles. We apply evaluative profiling to Reddit comments labeled with personality scores and conduct an exploratory study on the relationship between evaluative topics and Big Five personality facets, aiming for a more interpretable, facet-level analysis. Finally, we validate our approach by observing correlations consistent with prior research in personality psychology.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.294</identifier>
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        <date>2022-12</date>
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            <start>3986</start>
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%0 Conference Proceedings
%T You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis
%A Jukić, Josip
%A Vukojević, Iva
%A Snajder, Jan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F jukic-etal-2022-talk
%X Expressing attitude or stance toward entities and concepts is an integral part of human behavior and personality. Recently, evaluative language data has become more accessible with social media‘s rapid growth, enabling large-scale opinion analysis. However, surprisingly little research examines the relationship between personality and evaluative language. To bridge this gap, we introduce the notion of evaluative topics, obtained by applying topic models to pre-filtered evaluative text from social media. We then link evaluative topics to individual text authors to build their evaluative profiles. We apply evaluative profiling to Reddit comments labeled with personality scores and conduct an exploratory study on the relationship between evaluative topics and Big Five personality facets, aiming for a more interpretable, facet-level analysis. Finally, we validate our approach by observing correlations consistent with prior research in personality psychology.
%R 10.18653/v1/2022.findings-emnlp.294
%U https://aclanthology.org/2022.findings-emnlp.294/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.294
%P 3986-3999

Markdown (Informal)

[You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis](https://aclanthology.org/2022.findings-emnlp.294/) (Jukić et al., Findings 2022)

• You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis (Jukić et al., Findings 2022)

ACL

• Josip Jukić, Iva Vukojević, and Jan Snajder. 2022. You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3986–3999, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.