Suicide Detection using Twitter Sentiment Analysis

Life is indeed a precious gift, yet it is disheartening to acknowledge that each year, a staggering number of individuals, particularly among the younger generation, resort to taking their own lives, with over 800,000 suicides occurring globally. Despite the commendable efforts of organizations worldwide to provide counseling and support, a significant challenge persists the reluctance of those contemplating suicide to openly share their feelings with acquaintances, making it extremely challenging to distinguish a potentially suicidal person from a non-suicidal one through conventional means. In this digital age, the internet has provided people with numerous social media platforms to connect with others and share life updates. It has become increasingly common for individuals to express suicidal thoughts in their online posts before taking such drastic actions. Hence, this paper proposes the development of an advanced deep learning model that can analyze Twitter posts and classify them as" Suicidal" or" Non- suicidal This model leverages Convolutional Neural Networks (CNN's) to perform text classification on the tweets' content, extracting meaningful insights to detect signs of potential suicide risk. Such a system would be of immense value if implemented in real-life settings, enabling the early identification of profiles displaying suicidal tendencies, which, in turn, could contribute significantly to reducing the global suicide rate.