Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques

Ko, Y., Lee, J. H., & Song, M. (2021). Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques. 한국비블리아학회지, 32(3), 247-264, https://doi.org/10.14699/kbiblia.2021.32.3.247

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Abstract

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.

keywords: 자살, 소셜미디어, 단어 동시출현, 딥러닝, 토픽모델링, Suicide, Social media, Word Co-Occurrence, Deep-learning, Topic Modeling

Submission Date: 2021-08-16

Revised Date: 2021-08-17

Accepted Date: 2021-09-06

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Article Contents

Vol.32 No.3

Examining Suicide Tendency Social Media Texts by Deep Learning and Topic Modeling Techniques

Abstract

한국비블리아학회지