features.txt

"I developed a sentiment analysis web application using Flask, pandas, and TextBlob. The application allows users to input text and receive real-time sentiment analysis results. Key features include:

Web Framework: Utilized Flask to create a responsive and user-friendly web interface.
Data Processing: Leveraged pandas for efficient data manipulation and analysis.
Sentiment Analysis: Implemented sentiment analysis using TextBlob to classify text as positive, negative, or neutral.
CSV Handling: Enabled users to upload CSV files, process the data, and download the results.
Natural Language Processing (NLP): Integrated NLTK for advanced text preprocessing, including tokenization and stopword removal.
Temporary File Management: Utilized Python's tempfile module to securely handle temporary files during CSV processing.
User Experience: Designed and implemented a user-friendly interface with Flask templates and Bootstrap for responsive design.
Error Handling: Implemented robust error handling and validation to ensure smooth user experience and data integrity.
Performance Optimization: Optimized the application for performance, ensuring quick response times even with large datasets.
Testing: Developed unit tests to ensure the reliability and accuracy of the sentiment analysis functionality.
Version Control: Managed the project using Git and GitHub for version control and collaboration.
Deployment: Deployed the application using Gunicorn and Render, ensuring scalability and high availability.
Documentation: Created comprehensive documentation for the application, including setup instructions, usage guidelines, and API documentation."