🌟 lstm-sentiment-analysis - Simple Sentiment Analysis for Everyone

📖 About This Project

This project provides a complete solution for analyzing sentiment in text using a stacked Long Short-Term Memory (LSTM) model built in PyTorch. It includes custom tokenization, embeddings, and padding. The system handles class imbalance and offers thorough evaluation methods. Ideal for both beginners and those who want to refine their understanding of sentiment analysis.

🚀 Getting Started

This section will guide you through downloading and running the application step-by-step.

🌐 System Requirements

• Operating System: Windows, macOS, or Linux
• Python Version: 3.6 or higher
• Memory: At least 4 GB RAM recommended
• Disk Space: 200 MB available for installation
• Internet Connection: Required for downloading the software and necessary libraries

📥 Download & Install

To get started, visit the Releases page to download the application.

1. Click on the link above.
2. Locate the latest release version.
3. Download the file that suits your operating system.

⚙️ Installation Steps

1. Locate the Downloaded File: Navigate to your downloads folder to find the file you just downloaded.
2. Run the Installer:
   • For Windows: Double-click the .exe file.
   • For macOS: Open the .dmg file and drag the application to your Applications folder.
   • For Linux: Open a terminal and run the downloaded file using chmod +x filename followed by ./filename.

📊 Using the Application

Once installed, open the application.

1. Input Text: Type or paste the text you want to analyze into the input field.
2. Run Analysis: Click on the "Analyze" button.
3. View Results: The application will display whether the sentiment is positive, negative, or neutral.

🔍 Features

• Custom Tokenization: Breaks down sentences into manageable pieces for analysis.
• Embeddings: Utilizes advanced techniques to represent words in a numerical format.
• Padding: Ensures all input data has a consistent length.
• Class Imbalance Handling: Adjusts model training to account for uneven class distributions.
• Evaluation Metrics: Displays performance indicators such as accuracy and loss.

🧑‍🤝‍🧑 Who Can Use This?

This application is suitable for:

• Students learning about natural language processing (NLP)
• Business professionals wanting to analyze customer feedback
• Anyone interested in understanding sentiment analysis without deep technical knowledge

📚 Additional Resources

For further reading, you can explore:

• Deep Learning Courses: Look for online courses that cover sentiment analysis and LSTM models.
• PyTorch Documentation: Official resources to understand the PyTorch library better.
• NLP Tutorials: Find beginner guides to learn about natural language processing concepts.

🛠️ Troubleshooting

If you encounter problems:

• Check your system specifications to ensure compatibility.
• Ensure you have a stable internet connection during installation.
• Restart the application if it does not respond.

📞 Support

For any issues or questions, feel free to open an issue on the GitHub Repository. The community and maintainers are here to help.

🔗 Summary

Thank you for choosing the lstm-sentiment-analysis application. We're confident it will serve you well in understanding sentiments in text. Once again, here is the link to download the application. Enjoy analyzing sentiment!