The identification of individuals who indicate suicidal behaviors on social media platforms has become more significant in recent years. The utilization of textual data may help in the development of systems aimed at predicting individuals' mental health. This article proposes an integrated framework for the identification of suicidal thoughts in social media through the implementation of a layered classifier model consisting of a convolutional neural network (CNN) and a long short-term memory (LSTM) model. Various combinations of embedding techniques, activation functions, and solver algorithms are applied to the network. The mixture of these techniques forms 82 distinct methodologies employed, followed by comparing the results obtained. A collection of approximately 60,0000 user posts from 2018 to 2020 was compiled from Reddit for the study. It has resulted in the combination of TF-IDF (word embedding), RReLU (activation function), and Adam (solver algorithm) reaching the highest overall performance. The model achieved impressive accuracy, F1 Score, and AUC of 86%, with precision and recall score of 91% and 82% respectively. It was fitted in just 8.69 seconds, demonstrating its time efficiency as well. This approach has great potential for creating a platform in real life to not only reduce the social impacts of suicidality and mental illness, but also increase social access to mental health resources for all individuals. 

Adam, Activation Function, Bagging, Classification, CNN, LSTM, Machine Learning, Mental Health Disorder, Neural Network, NLP, Reddit, RNN, RReLU, Social Media, Stacking, Suicidality, Suicide Detection, Word Embedding