Suicidal tendencies prediction in Greek poetry

Abstract

Natural language processing (NLP) has been successfully used to predict a writer’s tendency of committing suicide, using various text types: suicide notes, micro-blog posts, lyrics and poems. This paper is an extended version of earlier work. We extend our previous work on text mining in Greek Poetry by employing more sophisticated approaches. More specifically we have applied (i) Deep Neural Networks (DNN), (ii) additional morphosyntactic and semantic features based on writers' emotions and Big Five personality traits and (iii) feature selection, for suicide prediction in Greek poetry. We extend previous research to Greek, i.e. a language that has not been tackled before in this setting, using both language-dependent (but easily portable across languages) and language-independent linguistic features in order to represent the poems of 13 Greek poets of the twentieth century. Our results differ significantly from previous literature. In general, our proposed DNN model offers promising results for suicide prediction, despite the fact that this task poses multiple difficulties, especially for a language with limited related research support.