@article{ART002758944},
author={YoungSoo Ko and Ju Hee Lee and Min Song},
title={Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques},
journal={Journal of the Korean Biblia Society for Library and Information Science},
issn={1229-2435},
year={2021},
volume={32},
number={3},
pages={247-264},
doi={10.14699/kbiblia.2021.32.3.247}
TY - JOUR
AU - YoungSoo Ko
AU - Ju Hee Lee
AU - Min Song
TI - Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques
JO - Journal of the Korean Biblia Society for Library and Information Science
PY - 2021
VL - 32
IS - 3
PB - Journal Of The Korean Biblia Society For Library And Information Science
SP - 247
EP - 264
SN - 1229-2435
AB - This study aims to create a deep learning-based classification model to classify suicide tendency by suicide corpus constructed for the present study. Also, to analyze suicide factors, the study classified suicide tendency corpus into detailed topics by using topic modeling, an analysis technique that automatically extracts topics. For this purpose, 2,011 documents of the suicide-related corpus collected from social media naver knowledge iN were directly annotated into suicide-tendency documents or non-suicide-tendency documents based on suicide prevention education manual issued by the Central Suicide Prevention Center, and we also conducted the deep learning model(LSTM, BERT, ELECTRA) performance evaluation based on the classification model, using annotated corpus data. In addition, one of the topic modeling techniques, LDA identified suicide factors by classifying thematic literature, and co-word analysis and visualization were conducted to analyze the factors in-depth.
KW - Suicide;Social media;Word Co-Occurrence;Deep-learning;Topic Modeling
DO - 10.14699/kbiblia.2021.32.3.247
ER -
YoungSoo Ko, Ju Hee Lee and Min Song. (2021). Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques. Journal of the Korean Biblia Society for Library and Information Science, 32(3), 247-264.
YoungSoo Ko, Ju Hee Lee and Min Song. 2021, "Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques", Journal of the Korean Biblia Society for Library and Information Science, vol.32, no.3 pp.247-264. Available from: doi:10.14699/kbiblia.2021.32.3.247
YoungSoo Ko, Ju Hee Lee, Min Song "Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques" Journal of the Korean Biblia Society for Library and Information Science 32.3 pp.247-264 (2021) : 247.
YoungSoo Ko, Ju Hee Lee, Min Song. Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques. 2021; 32(3), 247-264. Available from: doi:10.14699/kbiblia.2021.32.3.247
YoungSoo Ko, Ju Hee Lee and Min Song. "Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques" Journal of the Korean Biblia Society for Library and Information Science 32, no.3 (2021) : 247-264.doi: 10.14699/kbiblia.2021.32.3.247
YoungSoo Ko; Ju Hee Lee; Min Song. Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques. Journal of the Korean Biblia Society for Library and Information Science, 32(3), 247-264. doi: 10.14699/kbiblia.2021.32.3.247
YoungSoo Ko; Ju Hee Lee; Min Song. Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques. Journal of the Korean Biblia Society for Library and Information Science. 2021; 32(3) 247-264. doi: 10.14699/kbiblia.2021.32.3.247
YoungSoo Ko, Ju Hee Lee, Min Song. Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques. 2021; 32(3), 247-264. Available from: doi:10.14699/kbiblia.2021.32.3.247
YoungSoo Ko, Ju Hee Lee and Min Song. "Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques" Journal of the Korean Biblia Society for Library and Information Science 32, no.3 (2021) : 247-264.doi: 10.14699/kbiblia.2021.32.3.247
