Uncovering Depression with LSTM and NLP Transformers in Social Media Posts

Overview

This repository contains code and resources for detecting depression in social media posts using LSTM and NLP transformers like BERT. The project aims to leverage deep learning techniques for mental health monitoring by analyzing text data from social media.

Features

• LSTM Model: Utilized for sequence-based classification.
• BERT Model: Applied for advanced natural language understanding and comparison with LSTM.
• Data Preprocessing: Techniques to clean and prepare text data.
• Model Evaluation: Metrics like accuracy, F1-score, and confusion matrix.

Installation

1. Clone the repository:

   git clone https://github.com/anuraag165/Dep-LSTM-Transformer-NLP.git

2. Install the required packages:

   pip install -r requirements.txt

Usage

• Running the Notebook:
  • Open the BERT_v_LSTM.ipynb notebook to see the model implementation and comparison.
  • Ensure you have the necessary datasets in the data/ directory.

Results

• The repository contains visualizations and results comparing the performance of LSTM and BERT models in detecting depression.

Contributing

Feel free to fork the project, make improvements, and submit pull requests.

Programmer

• Anuraag Raj