  Suicide is a critical issue in modern society. Early detection and prevention
of suicide attempts should be addressed to save people's life. Current suicidal
ideation detection methods include clinical methods based on the interaction
between social workers or experts and the targeted individuals and machine
learning techniques with feature engineering or deep learning for automatic
detection based on online social contents. This paper is the first survey that
comprehensively introduces and discusses the methods from these categories.
Domain-specific applications of suicidal ideation detection are reviewed
according to their data sources, i.e., questionnaires, electronic health
records, suicide notes, and online user content. Several specific tasks and
datasets are introduced and summarized to facilitate further research. Finally,
we summarize the limitations of current work and provide an outlook of further
research directions.
