Stress Detection

Overview

This Python script showcases stress detection using natural language processing (NLP) techniques, including the creation of a word cloud. The script uses the nltk, scikit-learn, wordcloud, and matplotlib libraries. Users can input text data, and the script visualizes stress-related words and predicts stress levels. Explore this script to understand the basics of stress detection and visualization in Python.

Script Components

• Library Installation: Installing the necessary libraries (nltk, scikit-learn, wordcloud, matplotlib, and others if required).
• User Input: Code for user input to provide text data for stress detection.
• Text Preprocessing: Cleaning and preparing the text data for stress detection.
• Word Cloud Generation: Creating a word cloud to visualize stress-related words.
• NLP Model Training: Utilizing scikit-learn for text classification and stress level prediction.
• Output Display: Displaying the word cloud and stress level prediction.

Running the Script

1. Install the required libraries by running pip install nltk scikit-learn wordcloud matplotlib in your Python environment.
2. Open and run the Python script (stress_detection.py) in a Python environment.
3. Follow the on-screen prompts to input text data for stress detection.
4. The script will visualize a word cloud and predict stress levels based on the provided text.

Customization

• Replace the dataset or text input with your own stress-related data.
• Experiment with different text preprocessing techniques or machine learning models for stress detection.
• Modify the script to include additional features or visualizations.

License

This Stress Detection in Python is open-source and distributed under the MIT License. Feel free to modify and use the code for your stress detection projects or educational purposes!