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

Documents and Presentations

Repository

https://github.com/richiekenath/Depression_detection

Collaborators