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Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms

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  • Mauricio Toledo-Acosta

    (Computational Semantics Laboratory, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico)

  • Talin Barreiro

    (Computational Semantics Laboratory, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico)

  • Asela Reig-Alamillo

    (Cognitive Linguistics Laboratory, Centro de Investigación en Ciencias Cognitivas, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico)

  • Markus Müller

    (Complex Systems Laboratory, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico)

  • Fuensanta Aroca Bisquert

    (Instituto de Matemáticas, Unidad de Cuernavaca, Universidad Nacional Autónoma de México, Cuernavaca 62209, Morelos, Mexico
    CNRS-UMI 4584–LaSoL Laboratorio Internacional Solomon Lefschetz, Cuernavaca 62210, Morelos, Mexico)

  • Maria Luisa Barrigon

    (Department of Psychiatry, University Hospital Jimenez Diaz Foundation, 28050 Madrid, Spain)

  • Enrique Baca-Garcia

    (Department of Psychiatry, University Hospital Rey Juan Carlos, 28933 Mostoles, Spain
    Department of Psychiatry, General Hospital of Villalba, 28400 Madrid, Spain
    Department of Psychiatry, University Hospital Infanta Elena, 28342 Valdemoro, Spain
    Research Department, Universidad Católica del Maule, Talca 03550, Maule, Chile)

  • Jorge Hermosillo-Valadez

    (Computational Semantics Laboratory, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico)

Abstract

Mathematical modeling of language in Artificial Intelligence is of the utmost importance for many research areas and technological applications. Over the last decade, research on text representation has been directed towards the investigation of dense vectors popularly known as word embeddings. In this paper, we propose a cognitive-emotional scoring and representation framework for text based on word embeddings. This representation framework aims to mathematically model the emotional content of words in short free-form text messages, produced by adults in follow-up due to any mental health condition in the outpatient facilities within the Psychiatry Department of Hospital Fundación Jiménez Díaz in Madrid, Spain. Our contribution is a geometrical-topological framework for Sentiment Analysis, that includes a hybrid method that uses a cognitively-based lexicon together with word embeddings to generate graded sentiment scores for words, and a new topological method for clustering dense vector representations in high-dimensional spaces, where points are very sparsely distributed. Our framework is useful in detecting word association topics, emotional scoring patterns, and embedded vectors’ geometrical behavior, which might be useful in understanding language use in this kind of texts. Our proposed scoring system and representation framework might be helpful in studying relations between language and behavior and their use might have a predictive potential to prevent suicide.

Suggested Citation

  • Mauricio Toledo-Acosta & Talin Barreiro & Asela Reig-Alamillo & Markus Müller & Fuensanta Aroca Bisquert & Maria Luisa Barrigon & Enrique Baca-Garcia & Jorge Hermosillo-Valadez, 2020. "Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2088-:d:449305
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    References listed on IDEAS

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