Tweet Ideation - To save lives

Problem Statement

• Detecting suicide-related content on social media is challenging but crucial for timely intervention and support. This project addresses the need for an automated system to identify tweets expressing potential suicide risk.

Solution

• Classification Model: Utilizing Python, Pandas, Matplotlib, Seaborn, Scikit-Learn, and the NLTK library, this project aims to build a classification model capable of identifying tweets with potential suicide risk. The goal is to contribute to mental health awareness and support initiatives.

Technology Used

1. Python: Core programming language for its versatility and extensive libraries.
2. Pandas: Efficient data manipulation for preparing and analyzing datasets.
3. Matplotlib and Seaborn: Data visualization for insightful analysis.
4. Scikit-Learn: Building and training the classification model.
5. NLTK (Natural Language Toolkit): Utilized for advanced natural language processing tasks, enhancing the model's understanding of textual data.

Model Performance

• F1 Score (0.90): The model achieves an impressive F1 score of 0.90, indicating a high level of precision and recall balance. This metric showcases the model's effectiveness in correctly identifying tweets with suicide risk while minimizing false positives and false negatives.

Open the Notebook for More Details

• To explore the model and visualizations in detail, please follow these steps:
  1. Install Jupyter Notebook: If not already installed, run pip install notebook in your terminal or command prompt.
  2. Download the Notebook: Obtain the classification model notebook from the designated repository or source.
  3. Navigate to the Notebook's Directory: Open your terminal or command prompt, use cd to navigate to the directory where the notebook is located.
  4. Launch Jupyter Notebook: Type jupyter notebook in the terminal and press Enter to open a new tab in your web browser.
  5. Access the Notebook: In the Jupyter Notebook interface, navigate to the directory and click on the notebook file (with a .ipynb extension).
  6. Run the Notebook Cells: Once open, run each cell sequentially to observe the model's functionality and visualize the results.

This documentation provides a comprehensive overview of the tweet classification project, detailing the problem statement, solution, technology stack, and model performance. For more in-depth insights and exploration, please refer to the accompanying Jupyter Notebook.