Suicidal Tendency Detection from Twitter posts Using A Long Short Memory Vectors

In the era of digital expression, this paper delves into the critical task of identifying suicidal tendencies within the multifaceted narratives of Twitter. Employing LSTM neural networks, the research intricately examines the temporal dynamics of language in tweets to unveil subtle linguistic cues indicative of potential suicidal inclinations. The LSTM's unique capacity to capture long-term dependencies positions it as an ideal tool for unraveling the complexities of sequential data. The study outlines a comprehensive methodology spanning data collection, preprocessing, LSTM model construction, and rigorous evaluation metrics. Comparative analyses with other machine learning algorithms underscore the LSTM model's exceptional performance, achieving an accuracy of 92.3%. Notably, the system's design integrates Global Vectors Spaces (GVS) for word embeddings, showcasing a meticulous fusion of advanced technologies to comprehend and intervene in mental health challenges within the dynamic realm of social media.