VISIT DEPLOYED APP : HERE
KAGGLE CODE : HERE
MEDIUM ARTICLE : Here

📌 Project Description

This project is a Multi-Model Sentiment Evaluation App built using Streamlit. It allows users to analyze the sentiment of a given text using multiple sentiment analysis approaches at once. Instead of relying on a single model, the app compares predictions from: Rule-based models (VADER) Statistical models (TextBlob) Machine Learning models (Gaussian Naive Bayes, Random Forest, XGBoost) All model outputs are shown together, and a final sentiment decision is generated based on the combined result. This project is mainly focused on learning, comparison, and practical implementation of NLP sentiment techniques.

🛠 Tech Stack

• Python
• Streamlit
• NLTK (VADER)
• TextBlob
• Scikit-learn
• XGBoost
• TF-IDF Vectorization