%0 Conference Proceedings
%T Language Processing for Predicting Suicidal Tendencies: A Case Study in Greek Poetry
%+ Ionian University [Corfu]
%A Zervopoulos, Alexandros, Dimitrios
%A Geramanis, Evangelos
%A Toulakis, Alexandros
%A Papamichail, Asterios
%A Triantafylloy, Dimitrios
%A Tasoulas, Theofanis
%A Kermanidis, Katia
%Z Part 2: 8th Mining Humanistic Data Workshop
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Hersonissos, Greece
%Y John MacIntyre
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations
%V AICT-560
%P 173-183
%8 2019-05-24
%D 2019
%R 10.1007/978-3-030-19909-8_15
%K NLP
%K Suicide
%K Poetry
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-02363833/document
%2 https://inria.hal.science/hal-02363833/file/484534_1_En_15_Chapter.pdf
%L hal-02363833
%U https://inria.hal.science/hal-02363833
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-560