In this project, I developed a Sentiment Analysis Web App using deep learning (CNN) and traditional models to classify text sentiment with high accuracy.
The system includes a complete evaluation pipeline comparing CNN, LSTM, Logistic Regression, Random Forest, and Naive Bayes — analyzing performance across multiple iterations and datasets.
Key Highlights:
Built a Streamlit-based web app for real-time sentiment classification
Developed and evaluated multiple models for accuracy and F1-score
Created detailed analysis reports and prototype schematics
Project here → GitHub Repository (https://github.com/Imkaran04/Sentiment_Analysis_Web_App/tree/main)
Reports: Sentiment Analysis Report (PDF), Product Prototype Diagram
Tech Stack: Python, Streamlit, TensorFlow/Keras, Scikit-learn, Matplotlib 