BanglaSAPM: A Deep Learning Model for Suicidal Attempt Prediction Using Social Media Content in Bangla

Abstract

Nowadays, people are constantly expressing their emotions, thoughts, opinions, and daily activities on social media. Therefore, social media posts have become a powerful tool among psychiatrists for the early detection of suicidal tendencies. However, the automatic detection of suicidal posts has become a challenging problem among researchers in the field of Natural Language Processing (NLP). A significant number of previous works have been found in the literature for the automatic detection of suicidal posts in different languages such as English. However, little effort has been devoted to automatically detecting suicidal posts in low-resource languages like Bangla due to the lack of available datasets. In this study, we have created a noble Bangla suicidal posts dataset named BanglaSPD and compared the performance of various machine learning and deep learning models for suicide attempt prediction by training and evaluating with the dataset. Finally, we have found that a deep learning-based model with CNN+BiLSTM has outperformed with 0.61 F1-Score in Fasttext word embedding methods.