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🎭 AI Sentiment Analysis Dashboard A comprehensive sentiment analysis application built with advanced NLP techniques and deployed on Streamlit Cloud.

📚 Table of Contents

• 🚀 Live Demo
• ✨ Features
• 🤖 Machine Learning Pipeline
• 📊 Performance Metrics
• 🛠️ Technology Stack
• 📦 Installation & Setup
• 🚀 Deployment on Streamlit Cloud
• 📁 Project Structure
• 📸 Screenshots
• 🤝 Contributing
• 📊 Model Performance Details
• 🐛 Known Issues
• 🙏 Acknowledgments
• 📞 Contact

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🚀 Live Demo

Try the App Live!

✨ Features

🔍 Single Text Analysis

• Real-time sentiment prediction
• Confidence scores with probability breakdown
• Interactive visualizations
• Text preprocessing insights

📋 Batch Processing

• Analyze multiple texts simultaneously
• Batch results with summary statistics
• Export functionality

📈 Analytics Dashboard

• Historical analysis trends
• Confidence distribution charts
• Performance metrics
• Data insights

🕒 History Tracking

• Complete analysis history
• Timestamp tracking
• Pattern recognition

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🤖 Machine Learning Pipeline

Data Preprocessing

• Text normalization and cleaning
• Stopword removal
• Lemmatization
• N-gram feature extraction

Feature Engineering

• TF-IDF Vectorization: Advanced term frequency analysis
• Count Vectorization: Bag-of-words representation
• N-grams: Unigrams and bigrams for context

Model Selection

• Multiple algorithms tested:
  • Naive Bayes
  • Logistic Regression
  • Random Forest
  • Support Vector Machine
• Hyperparameter optimization
• Cross-validation for robust performance

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📊 Performance Metrics

Model	Accuracy	Precision	Recall	F1-Score
Best Model	70%+	0.85+	0.80+	0.80+

🛠️ Technology Stack

• Frontend: Streamlit
• Backend: Python 3.9+
• ML Libraries: scikit-learn, NLTK
• Visualization: Plotly, Matplotlib, Seaborn
• Deployment: Streamlit Cloud
• Version Control: Git & GitHub

📦 Installation & Setup

Prerequisites

• Python 3.9 or higher
• Git

🚀 Deployment on Streamlit Cloud

Quick Deployment Steps

1. Push to GitHub

   git add .
   git commit -m "Initial commit"
   git push origin main

2. Deploy on Streamlit Cloud

   • Visit share.streamlit.io
   • Connect your GitHub repository
   • Select your repository and branch
   • Set main file path: app.py
   • Click "Deploy!"

3. Automatic Updates

   • Any push to your GitHub repository will automatically update your deployed app

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📁 Project Structure

sentiment-analysis-dashboard/
│
├── app.py                          # Main Streamlit application
├── sentiment_analysis_model.pkl    # Trained ML model
├── requirements.txt               # Python dependencies
├── README.md                     # Project documentation
├── preprocessing.py              # Data preprocessing scripts
├── model_training.py            # Model training pipeline
│
├── data/                        # Data directory
│   ├── raw/                    # Raw datasets
│   └── processed/             # Cleaned datasets
│
├── notebooks/                  # Jupyter notebooks
│   ├── EDA.ipynb             # Exploratory Data Analysis
│   ├── preprocessing.ipynb   # Data preprocessing
│   └── modeling.ipynb       # Model development
│
└── assets/                   # Static assets
    ├── screenshots/         # App screenshots
    └── demo/               # Demo files

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📸 Screenshots

🔹 Main Dashboard

🔹 Analysis Results

🔹 History Tracker

🔹 Batch Processing

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

🛠️ Development Workflow

• Fork the repository
• Create your feature branch: git checkout -b feature/AmazingFeature
• Commit your changes: git commit -m 'Add AmazingFeature'
• Push to the branch: git push origin feature/AmazingFeature
• Open a Pull Request

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📊 Model Performance Details

Training Data

• Dataset Size: [(1440 Records, 5 Fields)]
• Features: Preprocessed text data
• Labels: Positive, Negative, Neutral

🧾 Example Input

"It's a very bad product, highly dissatisfied.. Specifically the colour is too whitish and unreal display.. Won't recommend to buy, instead go for M31.. It hangs as well within less than a month of purchase."

✅ Output

• Predicted Sentiment: Negative
• Confidence: 79.5%
• Top Keywords: bad, product, highly, dissatisfied, specifically, colour

Evaluation Results

Classification Report:
                precision    recall  f1-score   support

    Negative       0.75      0.75      0.75       102
     Neutral       0.25      0.30      0.30       40
    Positive       0.81      0.77      0.80       146

    accuracy                           0.72      288
   macro avg       0.61      0.61      0.60      288
weighted avg       0.71      0.70      0.71      288

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🐛 Known Issues

• Large text inputs (>10,000 characters) may take longer to process
• NLTK downloads required on first run

🙏 Acknowledgments

• NLTK for natural language processing tools
• scikit-learn for machine learning algorithms
• Streamlit for the amazing web framework
• Plotly for interactive visualizations

📞 Contact

Your Name - sathwikyalagala@gmail.com

Project Link: https://github.com/SathwikPatel12/SentimentAnalysis-Streamlit

Live Demo: https://sentimentanalysis-app-ctenkjk48urxzuudzft2pk.streamlit.app/

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