Potential use of text classification tools as signatures of suicidal behavior : a proof-of-concept study using Virginia Woolf’s personal writings

• Autor(a) principal: Berni, Gabriela de Ávila
• Data de Publicação: 2018
• Outros Autores: Ponte, Francisco Diego Rabelo da, Garcia, Diego Librenza, Boeira, Manuela Vianna, Kauer-Sant'Anna, Márcia, Passos, Ives Cavalcante, Kapczinski, Flávio Pereira
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/188777
• topic: Suicídio; Depressão; Algoritmos; Aprendizado de máquina

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.