Suicide has emerged as a pressing societal health concern in contemporary times. Suicidal intent refers to an individual`s contemplation of taking one’s own life, and such tragedies have far-reaching impacts on families, communities, and nations. The global standardized rate of suicides per population suggests that in 2022, there were approximately 903,450 completed suicides, alongside a staggering 18,069,000 cases of individuals having suicidal thoughts but not acting upon them. These distressing figures highlight the widespread nature of this issue, affecting people of all ages, nationalities, races, beliefs, socioeconomic backgrounds, and genders. Additionally, it is pertinent to acknowledge that depression, a prevalent mental disorder, can significantly hinder daily functioning and potentially contribute to developing suicidal thoughts. This work focuses on detecting suicidal intent by employing Machine Learning classifiers such as Support Vector Machines, Naive Bayes, Logistic Regression, and Random Forest. Furthermore, this research extends its analysis by incorporating Deep Learning classifiers such as Convolutional Neural Networks, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Bidirectional Encoder Representations from Transformers on six selected datasets. The primary aim is to identify signs of depression as a means to gauge the likelihood of suicidal thoughts. In addition to the classification algorithms, various features are extracted to provide insights into an individual`s emotional state and mindset. By combining these techniques, the study aims to improve the understanding and prediction of suicidal tendencies.

Depression, Suicide, Depression Detection, Suicidal Ideation, Text Classification, Machine Learning, Deep Learning.

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