Due to different mental, physical and psychological factors, the tendency of attempting suicide among the people who often feel depressed and lonely is increasing in an alarming rate. Depression is a common mental illness that can interfere with daily activities and lead to suicidal thoughts or attempts. Traditional diagnostic approaches used by mental health specialists can aid in determining a person's level of depression. From study it is notable that, the people with this kind of tendency try to express their feelings through various social media platforms as a text. People likes to post in his/her mother language. So, suicidal sentiment detection from text is needed to be done to prevent suicide by informing their relatives and other law & enforcement authorities. Here, we have tried to figure out a comparative study between machine learning and deep learning algorithms in the study of suicidal sentiment analysis. We have used several Machine learning approaches as well as deep learning algorithms. We also tried hyper-parameter tuning to improve the accuracy of our model, yet we have found the best result in default parameter values. We have also tried to develop a sequential Neural Network Model and Long Short-Term Memory model for the purpose of comparative study. Among all other models, We have got 94% accuracy from SVM model and 93.5% accuracy from Logistic Regression model. In deep learning methodology, sequential recurrent neural network has been used to calculate the value loss. Value loss is almost 3% because of vanishing gradient point and exploding gradient. To reduce the value loss and improve the accuracy we have used long short-term memory. The value loss of LSTM model is less than 1% and the accuracy is secured in 91%.

This paper is a NLP task on Suicide and Depression detection for Twitter messages

An AI-powered system for early detection of depression and suicide risk using NLP and deep learning, integrated with a React-based dashboard for real-time mental health monitoring.

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Suicide and Depression Detection is a deep learning-based web app that analyzes text (tweets, quotes, or posts) to detect suicidal or depressive content. Built using LSTM with GloVe embeddings and deployed with Streamlit, it demonstrates how NLP can support mental health awareness through early detection.

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A model that can be used to detect suicide and depression using a text.

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a model that identifieds the suicidal taughts in the reddit post corpus

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Reddit Sosyal Medyası Üzerindeki Kullanıcı Mesajlarının Makine Öğrenmesi Modelleri ve BERT Modeli Kullanılarak İntihar Veya İntihar Değil Tahmini

Natural Language Processing Project - Semester 4

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Text classfier built for detect suicide and depression based on data from Reddit.com

Suicide and Depression Detection using Machine Learning

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Explainable AI for Depression & Suicide Detection using RoBERTa, LIME, SHAP, and Attention Visualization

Transformer-based models (RoBERTa-base and DistilBERT) for predicting mental health risks from social media text. Classifies posts into normal, anxiety, depression, loneliness, and suicide categories to enable early detection and intervention.

HeOur project detects signs of depression by analyzing Instagram posts using both image and text analysis. It combines Optical Character Recognition (OCR) and Natural Language Processing (NLP) to assess emotional states, offering early detection of depression and aiding in mental health monitoring, with potential applications in suicide prevention.

Cyberbullying is a growing issue in the digital age, impacting both teenagers and adults, often leading to severe consequences such as depression and suicide. This study focuses on the detection of cyberbullying in text data using Natural Language Processing (NLP) and Machine Learning techniques. Utilizing data from two sources

**MindWatchAI** is an AI-powered mental health risk detection platform built for Hacknovate 7.0 at ABES Institute of Technology. It analyzes conversations, emotions, and behavior patterns using machine learning to detect early signs of stress, depression, or suicide risk, helping counselors enable timely mental health support.