Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms
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 t...
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MDPI AG
2020-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/8/11/2088 |
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| author | Mauricio Toledo-Acosta Talin Barreiro Asela Reig-Alamillo Markus Müller Fuensanta Aroca Bisquert Maria Luisa Barrigon Enrique Baca-Garcia Jorge Hermosillo-Valadez |
| author_facet | Mauricio Toledo-Acosta Talin Barreiro Asela Reig-Alamillo Markus Müller Fuensanta Aroca Bisquert Maria Luisa Barrigon Enrique Baca-Garcia Jorge Hermosillo-Valadez |
| author_sort | Mauricio Toledo-Acosta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:39:12Z |
| format | Article |
| id | doaj.art-aae6df33985a4155957bca8de171a493 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T14:39:12Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-aae6df33985a4155957bca8de171a4932023-11-20T21:55:59ZengMDPI AGMathematics2227-73902020-11-01811208810.3390/math8112088Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric SymptomsMauricio Toledo-Acosta0Talin Barreiro1Asela Reig-Alamillo2Markus Müller3Fuensanta Aroca Bisquert4Maria Luisa Barrigon5Enrique Baca-Garcia6Jorge Hermosillo-Valadez7Computational Semantics Laboratory, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoComputational Semantics Laboratory, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoCognitive Linguistics Laboratory, Centro de Investigación en Ciencias Cognitivas, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoComplex Systems Laboratory, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoInstituto de Matemáticas, Unidad de Cuernavaca, Universidad Nacional Autónoma de México, Cuernavaca 62209, Morelos, MexicoDepartment of Psychiatry, University Hospital Jimenez Diaz Foundation, 28050 Madrid, SpainDepartment of Psychiatry, University Hospital Rey Juan Carlos, 28933 Mostoles, SpainComputational Semantics Laboratory, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoMathematical 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.https://www.mdpi.com/2227-7390/8/11/2088cognitive-emotional embedded representationstopological-geometrical clusteringsuicide ideation prediction |
| spellingShingle | Mauricio Toledo-Acosta Talin Barreiro Asela Reig-Alamillo Markus Müller Fuensanta Aroca Bisquert Maria Luisa Barrigon Enrique Baca-Garcia Jorge Hermosillo-Valadez Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms Mathematics cognitive-emotional embedded representations topological-geometrical clustering suicide ideation prediction |
| title | Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms |
| title_full | Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms |
| title_fullStr | Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms |
| title_full_unstemmed | Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms |
| title_short | Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms |
| title_sort | cognitive emotional embedded representations of text to predict suicidal ideation and psychiatric symptoms |
| topic | cognitive-emotional embedded representations topological-geometrical clustering suicide ideation prediction |
| url | https://www.mdpi.com/2227-7390/8/11/2088 |
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