Depression detector
- 0 Collaborators
In this project, we aim to develop a machine-learning model that can accurately predict the likelihood of depression based on social media posts. We will gather a large dataset of social media posts from individuals with and without depression and use natural language processing techniques. ...learn more
Project status: Under Development
oneAPI, Artificial Intelligence, Cloud
Overview / Usage
Using machine learning to detect early signs of depression in social media posts
Methodology / Approach
This is a stand alone module which requires the user to enter a dataset location with 2 folder ,preferably labeled positive and negative,with positive folder containing positively identified depressive tweets and the negative folder containing normal tweets
a sample dataset is provided in the data folder ,labeled , dataset_tweets_rm.zip to use the dataset,extract the above mentioned file and enter the location of the file at the prompt
this module creates a pickle file which contains a dictionary of all the preprocessed tweets along with their labels and vectors
Technologies Used
Python, nlp, pandas, os, pickle, io, demoji, nltk, re, string, random, numpy, tensorflow, fasttextasft, keras, matplotlib, sklearn, demoji, oneapi, artificial intelligence