Abstract:
Depression is a major public health concern that can have significant negative impacts on an
individual's quality of life. Early detection of depression can be crucial for facilitating timely
treatment and improving outcomes. In this study, we aimed to investigate the use of machine
learning algorithms for detecting depression in social media comments written in Bengali, a
language spoken by millions of people around the world. We collected a dataset of social media
comments written in Bengali and labeled them according to the emotional state of the person
posting (e.g., happy, sad, or depressed). We describe the development and evaluation of several
different algorithms, including SVM, LR, DT, KNN, CB, and LR. The results of our evaluation
showed that the SVM algorithm had the highest accuracy, receiving a 75.28% score and being
able to detect depression with high accuracy, suggesting that social media comments written in
Bengali could be a useful source of data for detecting depression. These findings could have
important implications for the development of automated tools for detecting depression in
real-time and facilitating timely treatment.
