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

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.