https://github.com/vathsan08/mental-health-sentiment-analysis-using-deep-learning
# Mental Health Sentiment Analysis using Deep LearningThis project leverages deep learning to classify mental health-related sentiments from text into seven categories: Anxiety, Bipolar, Depression, Normal, Personality Disorder, Stress, and Suicidal. By utilizing advanced NLP techniques, we aim to enhance understanding and support for mental well
https://github.com/vathsan08/mental-health-sentiment-analysis-using-deep-learning
audio-classification eda emotion-detection l2-regularization logistic-regression machine-learning meachine-learning mental-health ngrams nltk pytorch roberta sentiment-analysis spacy speech-emotion-recognition speech-recognition transformer-models urdu-language-processing
Last synced: 4 months ago
JSON representation
# Mental Health Sentiment Analysis using Deep LearningThis project leverages deep learning to classify mental health-related sentiments from text into seven categories: Anxiety, Bipolar, Depression, Normal, Personality Disorder, Stress, and Suicidal. By utilizing advanced NLP techniques, we aim to enhance understanding and support for mental well
- Host: GitHub
- URL: https://github.com/vathsan08/mental-health-sentiment-analysis-using-deep-learning
- Owner: VATHSAN08
- License: mit
- Created: 2025-05-22T16:52:17.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-06T00:30:05.000Z (4 months ago)
- Last Synced: 2025-06-06T01:29:29.913Z (4 months ago)
- Topics: audio-classification, eda, emotion-detection, l2-regularization, logistic-regression, machine-learning, meachine-learning, mental-health, ngrams, nltk, pytorch, roberta, sentiment-analysis, spacy, speech-emotion-recognition, speech-recognition, transformer-models, urdu-language-processing
- Language: Jupyter Notebook
- Size: 4.12 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Mental Health Sentiment Analysis using Deep Learning 🧠❤️
Welcome to the **Mental Health Sentiment Analysis using Deep Learning** repository! This project leverages advanced deep learning techniques to analyze sentiments related to mental health from textual data. Our goal is to provide early insights and support for mental health awareness.
## Table of Contents
- [Project Overview](#project-overview)
- [Getting Started](#getting-started)
- [Project Structure](#project-structure)
- [Technologies Used](#technologies-used)
- [Data Exploration and Analysis](#data-exploration-and-analysis)
- [Model Training](#model-training)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)
- [Releases](#releases)
## Project Overview
Mental health is a critical aspect of overall well-being. This project focuses on classifying sentiments from text related to mental health issues. By using a fine-tuned RoBERTa model, we aim to detect emotions and sentiments effectively. This can help in providing timely support and resources to individuals in need.
## Getting Started
To get started with this project, follow these steps:
1. **Clone the Repository**:
```bash
git clone https://github.com/VATHSAN08/Mental-Health-Sentiment-Analysis-using-Deep-Learning.git
cd Mental-Health-Sentiment-Analysis-using-Deep-Learning
```
2. **Install Required Packages**:
Make sure you have Python installed. Then, install the required packages using pip:
```bash
pip install -r requirements.txt
```
3. **Download the Dataset**:
The dataset is available in the `data` folder. Ensure that you have the necessary files before proceeding.
4. **Run the Application**:
You can run the main script using:
```bash
python main.py
```
## Project Structure
The repository has the following structure:
```
Mental-Health-Sentiment-Analysis-using-Deep-Learning/
│
├── data/ # Contains datasets
│ ├── train.csv
│ ├── test.csv
│
├── notebooks/ # Jupyter notebooks for exploration
│ ├── EDA.ipynb
│
├── src/ # Source code
│ ├── model.py # Model definition and training
│ ├── utils.py # Utility functions
│
├── requirements.txt # Required packages
├── main.py # Main execution script
└── README.md # Project documentation
```
## Technologies Used
This project employs a variety of technologies and libraries, including:
- **Deep Learning Frameworks**: PyTorch
- **Natural Language Processing**: NLTK, SpaCy
- **Data Analysis**: Pandas, NumPy
- **Visualization**: Matplotlib, Seaborn
- **Modeling**: RoBERTa, Logistic Regression
- **Development Tools**: Jupyter Notebook, Git
## Data Exploration and Analysis
Understanding the data is crucial for building an effective model. In this project, we conduct exploratory data analysis (EDA) to visualize and understand the distribution of sentiments.
### Key Steps in EDA:
- **Data Cleaning**: Remove duplicates, handle missing values.
- **Text Preprocessing**: Tokenization, stemming, and lemmatization.
- **Sentiment Distribution**: Visualize the distribution of different sentiments.
You can find the EDA process in the Jupyter notebook located in the `notebooks` folder.
## Model Training
The core of this project revolves around training the RoBERTa model. Here’s how the training process works:
1. **Data Preparation**: Split the data into training and validation sets.
2. **Model Configuration**: Define the architecture and hyperparameters.
3. **Training Loop**: Train the model using the training set while validating on the validation set.
4. **Evaluation**: Assess the model's performance using metrics like accuracy and F1 score.
For detailed implementation, check the `model.py` file in the `src` directory.
## Usage
After training the model, you can use it to predict sentiments on new text data. Here’s a simple example:
```python
from src.model import load_model, predict_sentiment
model = load_model('path_to_model')
text = "I feel overwhelmed and anxious."
sentiment = predict_sentiment(model, text)
print(f"The sentiment is: {sentiment}")
```
## Contributing
We welcome contributions to improve this project. If you have suggestions or find bugs, please create an issue or submit a pull request.
### Steps to Contribute:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature/YourFeature`).
3. Make your changes.
4. Commit your changes (`git commit -m 'Add some feature'`).
5. Push to the branch (`git push origin feature/YourFeature`).
6. Open a pull request.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.
## Acknowledgments
- Special thanks to the contributors and researchers in the field of mental health and machine learning.
- This project builds upon the work of various open-source libraries and frameworks.
## Releases
For downloadable versions of the project, visit the [Releases](https://github.com/VATHSAN08/Mental-Health-Sentiment-Analysis-using-Deep-Learning/releases) section. Here, you can find pre-built packages and versions of the code.
---
Feel free to explore the repository and engage with the community. Together, we can make a difference in mental health awareness and support.