Suicidal Text Analysis
Table of Contents
About
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.
Getting Started
Clone this dataset to get started.
https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch
and put it in a folder called Data
Prerequisites
Python or Anaconda
Deployment
https://share.streamlit.io/faiqali1/suicidal-text-analysis/main/stream.py
Deployment on https://share.streamlit.io/