Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings.
BACKGROUND:The present study analyzes the feasibility of text classification to predict individual suicidal behavior. Entries from Virginia Woolf's diaries and letters were used to assess whether a text classification algorithm could identify written patterns associated with suicide. METHODS:Th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6200194?pdf=render |
| _version_ | 1828748982343434240 |
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| author | Gabriela de Ávila Berni Francisco Diego Rabelo-da-Ponte Diego Librenza-Garcia Manuela V Boeira Márcia Kauer-Sant'Anna Ives Cavalcante Passos Flávio Kapczinski |
| author_facet | Gabriela de Ávila Berni Francisco Diego Rabelo-da-Ponte Diego Librenza-Garcia Manuela V Boeira Márcia Kauer-Sant'Anna Ives Cavalcante Passos Flávio Kapczinski |
| author_sort | Gabriela de Ávila Berni |
| collection | DOAJ |
| description | BACKGROUND:The present study analyzes the feasibility of text classification to predict individual suicidal behavior. Entries from Virginia Woolf's diaries and letters were used to assess whether a text classification algorithm could identify written patterns associated with suicide. METHODS:This is a text classification study. We compared 46 text entries from the two months before Virginia Woolf's suicide with 54 texts randomly selected from Virginia Woolf's work during other periods of her life. Letters and diaries were included, while books, novels, short stories, and article fragments were excluded. The data was analyzed using a Naïve-Bayes machine-learning algorithm. RESULTS:The model showed a balanced accuracy of 80.45%, sensitivity of 69%, and specificity of 91%. The Kappa statistic was 0.6, which means a good agreement, and the p-value of the model was 0.003. The area under the ROC curve (AUC) was 0.80. In other words, the model exhibited good performance when used for classifying Virginia Woolf's diaries and letters. DISCUSSION:The present study showed the feasibility of a machine-learning model coupled with text to identify individual written patterns associated with suicidal behavior. Our text signature was able to identify the period of two months preceding suicide with a high accuracy. This technique may be applied to subjects with psychiatric disorders by means of data captured from social media, e-mail, among others. The algorithm may then predict a specific outcome and enable early intervention by clinicians. |
| first_indexed | 2024-12-10T11:44:50Z |
| format | Article |
| id | doaj.art-cc4e690ab28949f893b0fec1cea2c8a0 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T11:44:50Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-cc4e690ab28949f893b0fec1cea2c8a02022-12-22T01:50:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011310e020482010.1371/journal.pone.0204820Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings.Gabriela de Ávila BerniFrancisco Diego Rabelo-da-PonteDiego Librenza-GarciaManuela V BoeiraMárcia Kauer-Sant'AnnaIves Cavalcante PassosFlávio KapczinskiBACKGROUND:The present study analyzes the feasibility of text classification to predict individual suicidal behavior. Entries from Virginia Woolf's diaries and letters were used to assess whether a text classification algorithm could identify written patterns associated with suicide. METHODS:This is a text classification study. We compared 46 text entries from the two months before Virginia Woolf's suicide with 54 texts randomly selected from Virginia Woolf's work during other periods of her life. Letters and diaries were included, while books, novels, short stories, and article fragments were excluded. The data was analyzed using a Naïve-Bayes machine-learning algorithm. RESULTS:The model showed a balanced accuracy of 80.45%, sensitivity of 69%, and specificity of 91%. The Kappa statistic was 0.6, which means a good agreement, and the p-value of the model was 0.003. The area under the ROC curve (AUC) was 0.80. In other words, the model exhibited good performance when used for classifying Virginia Woolf's diaries and letters. DISCUSSION:The present study showed the feasibility of a machine-learning model coupled with text to identify individual written patterns associated with suicidal behavior. Our text signature was able to identify the period of two months preceding suicide with a high accuracy. This technique may be applied to subjects with psychiatric disorders by means of data captured from social media, e-mail, among others. The algorithm may then predict a specific outcome and enable early intervention by clinicians.http://europepmc.org/articles/PMC6200194?pdf=render |
| spellingShingle | Gabriela de Ávila Berni Francisco Diego Rabelo-da-Ponte Diego Librenza-Garcia Manuela V Boeira Márcia Kauer-Sant'Anna Ives Cavalcante Passos Flávio Kapczinski Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings. PLoS ONE |
| title | Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings. |
| title_full | Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings. |
| title_fullStr | Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings. |
| title_full_unstemmed | Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings. |
| title_short | Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings. |
| title_sort | potential use of text classification tools as signatures of suicidal behavior a proof of concept study using virginia woolf s personal writings |
| url | http://europepmc.org/articles/PMC6200194?pdf=render |
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