Characterizing Suicide Ideation by Using Mental Disorder Features on Microblogs: A Machine Learning Perspective

Samer Muthana Sarsam*, Hosam Al-Samarraie, Ahmed Ibrahim Alzahrani, Su Mon Chit, Abdul Samad Shibghatullah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
JournalInternational Journal of Mental Health and Addiction
Early online date14 Nov 2022
Publication statusE-pub ahead of print - 14 Nov 2022


  • Sentiment analysis
  • Suicide ideation
  • Topic modeling
  • Twitter

ASJC Scopus subject areas

  • Psychiatry and Mental health


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