🚀 Project Overview
This project is an AI-based Sentiment & Emotion Analysis System that detects the emotional tone of a given text. It uses Natural Language Processing (NLP) and Machine Learning techniques to classify user input into sentiment/emotion categories with high accuracy.
The system also includes a Streamlit web interface for real-time interaction and follows DevOps best practices such as Git version control and modular project structure.
🎯 Objectives
Analyze user-provided text and detect sentiment/emotion
Apply NLP preprocessing and feature extraction
Train and evaluate a machine learning classification model
Provide real-time predictions via a web interface
Maintain clean, deployment-ready code using Git
🛠️ Tech Stack
Programming Language: Python
Libraries & Tools:
Pandas, NumPy
Scikit-learn
TF-IDF Vectorizer
Logistic Regression / Random Forest (as used)
Pickle (model persistence)
Streamlit (web UI)
Version Control: Git & GitHub
📂 Project Structure sentiment-analysis/ │ ├── data/ │ └── cleaned_dataset.csv │ ├── models/ │ ├── sentiment_model.pkl │ └── vectorizer.pkl │ ├── app.py # Streamlit web app ├── train_model.py # Model training script ├── preprocess.py # Text preprocessing ├── requirements.txt └── README.md
⚙️ Workflow
Text Preprocessing
Convert text to lowercase
Remove special characters and noise
Handle missing or empty values
Feature Extraction
Apply TF-IDF Vectorization to convert text into numerical features
Model Training
Train a machine learning classifier on labeled data
Evaluate model accuracy and performance
Prediction
Load trained model and vectorizer
Predict sentiment/emotion for user input text
Web Interface
Streamlit app allows real-time text input
Displays predicted emotion instantly
2️⃣ Install Dependencies pip install -r requirements.txt
3️⃣ Run the Streamlit App streamlit run app.py
📈 Output Example
Input:
"I am feeling very happy and motivated today!"
Output: ✅ Predicted Emotion: Happy 📊 Confidence Score: 92%
🔐 DevOps & Git Practices
Feature-based development
Regular commits with meaningful messages
Clean project structure
Deployment-ready codebase
🌟 Expected Outcome
Accurate emotion detection from text
Interactive and user-friendly UI
Hands-on experience with NLP, ML, and deployment concepts
Strong project for placements, internships, and GitHub portfolio
🔮 Future Enhancements
Add more emotion classes
Use deep learning models (LSTM / Transformers)
Deploy on cloud platforms (AWS / Render / Hugging Face)
Add user authentication and history tracking
Support multiple languages
👤 Author
Kunal Kumar B.Tech CSE | Data Science & Full Stack Enthusiast 📌 Aspiring Data Scientist / ML Engineer