🧠 Sentiment Analyzer Web App

A simple NLP project that uses a trained machine learning model to predict the sentiment of user input text (positive, negative, neutral) using Flask and Tailwind CSS.

💡 Features

• Takes user input in a web form
• Predicts sentiment using a trained ML model
• Shows emoji-based feedback and explanation
• Clean Tailwind CSS interface
• Works with browser favicon using emoji 🧠

📦 Project Structure

Sentiment_Analyzer/ │ ├── data/ │ ├── twitter_training.csv │ └── twitter_validation.csv │ ├── model/ │ ├── label_encoder.pkl │ ├── sentiment_model.pkl │ ├── vectorizer.pkl │ └── train_model.ipynb │ ├── templates/ │ └── index.html # Frontend UI │ ├── Screenshots/ │ ├── Preview1.png │ ├── Preview2.png │ ├── Preview3.png │ └── Preview4.png │ ├── app.py # Flask backend ├── requirements.txt └── README.md

🧠 Model Info

The model was trained on a Twitter sentiment dataset using TF-IDF vectorization + Logistic Regression classifier.

##🛠️ Technologies Used

• Python – Programming language used for backend and model training
• Flask – Lightweight web framework for building the application interface
• NLTK – Natural Language Toolkit for text preprocessing and cleaning
• Scikit-learn – For building and training the sentiment classification model
• Pandas – Handling and processing dataset (CSV files)
• Joblib – Saving and loading trained ML models and vectorizers
• Tailwind CSS – For modern, responsive frontend styling (via CDN)
• HTML5 – Markup language for frontend UI
• Jupyter Notebook – For training and experimenting with the model

🚀 How to Run the Project

1. Clone the repository

git clone https://github.com/your-username/sentiment-analyzer.git
cd sentiment-analyzer

2. Install dependencies

pip install -r requirements.txt

3. (Optional) Train the model if not already done

# Open Jupyter Notebook and run all cells to train the model
jupyter notebook model/train_model.ipynb

4. Start the Flask app

python app.py

✅ Go to your browser and open: http://127.0.0.1:5000/

📦 Dependencies

Flask scikit-learn pandas joblib nltk notebook

📚 Dataset Used

Twitter sentiment dataset (twitter_training.csv and twitter_validation.csv) - From Kaggle