Abstract
Social media is a relatively new phenomenon that has swept the world during the past decade. With the increase in the number of people joining the virtual bandwagon, huge amount of unstructured text is being generated. These texts can prove to be very useful in comprehending the mental state of the user and in predicting one's level of depression and suicide ideation. This paper analyzes Reddit posts to identify users with poor mental health conditions who are on the verge of inflicting self-harm or committing suicide. In the process, Machine Learning models are built using six different classification techniques and Sentiment Analysis is performed to extract features that depict the emotional mindset of an online user. Naïve Bayes classifier emerged as a stable classifier with a precision value of 71.40%, thus showing an affirmative cue in solving the task of suicide risk assessment.
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Gupta, S., Das, D., Chatterjee, M., Naskar, S. (2021). Machine Learning-Based Social Media Analysis for Suicide Risk Assessment. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1300. Springer, Singapore. https://doi.org/10.1007/978-981-33-4367-2_37
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DOI: https://doi.org/10.1007/978-981-33-4367-2_37
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