• Dr. P. Venkateswara Rao
• Ch. SLVN.Gayathri

Suicide is a critical issue in modern society. There are several factors that can lead a person to commit a suicide, for instance, stress, depression, failure, disappointment, pessimism, unemployment, among others. The adverse effects of suicide in our society are not only emotional but also economic. A recent study by the Centre for Disease Control (CDC) of the United States of America estimated that the economic toll of suicide on society is immense as well. Suicides and suicide attempts cost the nation almost $70 billion per year in lifetime medical and work-loss costs alone. Detecting and preventing suicide attempts therefore becomes crucial for the authorities of the land and should be addressed in order to save people’s lives and preserve the social fabric of the community. Nowadays social media has become a way for people to express themselves and thus may be used to convey suicidal tendencies. Due to the anonymity of online media and social networks, people tend to express their feelings and sufferings in various online communities. Potentially suicidal individuals also use social forum platforms to discuss their problems or get access to information related to their condition. Traditional modes of prevention include interactions between likely suicidal individuals and an expert, a therapist or a social worker. But this mode of operations is susceptible to create some delays in the diagnosis of the patient, which could lead to a fatality. In order to prevent suicides more effectively, the ideation must be detected as early as possible. This can be done by analyzing users' posts for suicidal related content. The key objective being to present an automatic recognition of suicidal posts using Machine learning techniques. We focus on the online communities of Reddit and Twitter. The project will consist of developing a model of classification of various social media posts into classes that determine whether the user has suicidal tendencies or not

Stress, Stress Detection, Social Media (SM), Machine Learning (ML), Natural Language Processing (NLP), Supervised Learning, Support Vector Machine (SVM), Decision Tree, NLTK.

"Detecting Stress Posts in Social Networks Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.576-582, June-2024, Available :http://www.jetir.org/papers/JETIRGL06096.pdf

"Detecting Stress Posts in Social Networks Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp576-582, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06096.pdf