The suicide rate of patients with depression has been increasing in recent years. We trained this model by analyzing 232,074 pieces of text data. It is a machine learning-based algorithm for text classification and a computational method for semantic sentiment analysis. The experimental results show that it can effectively predict the sentiment of depression patients’ blog posts on social media such as Twitter or Facebook. This allows physicians to intervene in advance when a depressed patient attempts to harm himself.
Clone this dataset to get started.
https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch
and put it in a folder called Data
Python or Anaconda
https://share.streamlit.io/faiqali1/suicidal-text-analysis/main/stream.py
Deployment on https://share.streamlit.io/