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Conference Papers Year : 2019

Language Processing for Predicting Suicidal Tendencies: A Case Study in Greek Poetry

Alexandros Dimitrios Zervopoulos
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Evangelos Geramanis
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Alexandros Toulakis
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Asterios Papamichail
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Dimitrios Triantafylloy
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Theofanis Tasoulas
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Katia Kermanidis
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Natural language processing has previously been used with fairly high success to predict a writer’s likelihood of committing suicide, using a wide variety of text types, including suicide notes, micro-blog posts, lyrics and even poems. In this study, we extend work done in previous research to a language that has not been tackled before in this setting, namely Greek. A set of language-dependent (but easily portable across languages) and language-independent linguistic features is proposed to represent the poems of 13 Greek poets of the 20th century. Prediction experiments resulted in an overall classification rate of 84.5% with the C4.5 algorithm, after having tested multiple machine-learning algorithms. These results differ significantly from previous research, as some features investigated did not play as significant a role as was expected. This kind of task presents multiple difficulties, especially for a language where no previous research has been conducted. Therefore, a significant part of the annotation process was performed manually, which likely explains the somewhat higher classification rates compared to previous efforts.


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Dates and versions

hal-02363833 , version 1 (14-11-2019)





Alexandros Dimitrios Zervopoulos, Evangelos Geramanis, Alexandros Toulakis, Asterios Papamichail, Dimitrios Triantafylloy, et al.. Language Processing for Predicting Suicidal Tendencies: A Case Study in Greek Poetry. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.173-183, ⟨10.1007/978-3-030-19909-8_15⟩. ⟨hal-02363833⟩
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