immaksudalam/A-Comparative-Study-on-Suicidal-Ideation-Detection-Using-Machine-Learning-and-Deep-Learning-Approach

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%.

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