TY - JOUR
T1 - Characterizing Suicide Ideation by Using Mental Disorder Features on Microblogs
T2 - A Machine Learning Perspective
AU - Sarsam, Samer Muthana
AU - Al-Samarraie, Hosam
AU - Alzahrani, Ahmed Ibrahim
AU - Chit, Su Mon
AU - Shibghatullah, Abdul Samad
N1 - Publisher Copyright:
© Crown 2022.
PY - 2024/8
Y1 - 2024/8
N2 - Despite the success of psychological and clinical methods, psychological studies revealed that the number of individuals exhibiting suicide ideation has highly increased in the recent decades. This study explored the potential of using certain sentimental features as a means for characterizing suicide. A total of 54,385 English-language tweets were collected and processed to extract suicide-related topics using the Latent Dirichlet Allocation (LDA) algorithm. Both suicidal polarity (positive, negative, and neutral) and emotions (anger, fear, sadness, and trust) were extracted via SentiStrength, time series, and NRC Affect Intensity Lexicon methods. The results showed that suicidal tweets were less associated with trust, anger, and positive sentiments. In contrast, fear, sadness, and negative sentiments were highly associated with suicidal statements. The prediction results using this approach showed 97.64% accuracy in detecting suicide ideation. The obtained results from analyzing suicide-related tweets hold a promising future for characterizing suicide ideation worldwide.
AB - Despite the success of psychological and clinical methods, psychological studies revealed that the number of individuals exhibiting suicide ideation has highly increased in the recent decades. This study explored the potential of using certain sentimental features as a means for characterizing suicide. A total of 54,385 English-language tweets were collected and processed to extract suicide-related topics using the Latent Dirichlet Allocation (LDA) algorithm. Both suicidal polarity (positive, negative, and neutral) and emotions (anger, fear, sadness, and trust) were extracted via SentiStrength, time series, and NRC Affect Intensity Lexicon methods. The results showed that suicidal tweets were less associated with trust, anger, and positive sentiments. In contrast, fear, sadness, and negative sentiments were highly associated with suicidal statements. The prediction results using this approach showed 97.64% accuracy in detecting suicide ideation. The obtained results from analyzing suicide-related tweets hold a promising future for characterizing suicide ideation worldwide.
KW - Sentiment analysis
KW - Suicide ideation
KW - Topic modeling
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85141951658&partnerID=8YFLogxK
U2 - 10.1007/s11469-022-00958-z
DO - 10.1007/s11469-022-00958-z
M3 - Article
AN - SCOPUS:85141951658
SN - 1557-1874
VL - 22
SP - 1783
EP - 1796
JO - International Journal of Mental Health and Addiction
JF - International Journal of Mental Health and Addiction
IS - 4
ER -