Sentiment Analysis System

Project Overview

Building an end-to-end machine learning pipeline to classify customer feedback sentiment using Natural Language Processing techniques. This project focuses on creating a reproducible pipeline and exploring model versioning and monitoring practices as part of my MLOps learning journey.

Learning Goals

This is a hands-on learning project designed to build practical experience with:

• End-to-end ML workflow from data preprocessing to deployment
• Model evaluation and performance metrics
• Deployment strategies and infrastructure planning
• MLOps best practices (versioning, monitoring, reproducibility)

Current Progress

✅ Completed

• Data collection and exploratory data analysis
• Text preprocessing pipeline (cleaning, tokenization, vectorization)
• Baseline model training with scikit-learn
• Initial model evaluation metrics

🔄 In Progress

• Deep learning model with TensorFlow
• Hyperparameter tuning and optimization
• Model versioning setup

📋 Planned

• Deployment infrastructure with AWS SageMaker
• Model monitoring and drift detection
• CI/CD pipeline for model retraining

Technical Approach

Data Preprocessing

The preprocessing pipeline includes:

• Text Cleaning: Removing special characters, URLs, and noise
• Normalization: Lowercasing, lemmatization, and standardization
• Tokenization: Breaking text into meaningful tokens
• Feature Extraction: TF-IDF vectorization and word embeddings
• Data Splitting: Stratified train/validation/test split to ensure balanced classes

Model Development

Baseline Model:

• Logistic Regression with TF-IDF features
• Provides a strong baseline for comparison
• Fast training and inference

Advanced Model:

• LSTM/Transformer-based architecture with TensorFlow
• Word embeddings (Word2Vec or pre-trained like GloVe)
• Multi-layer neural network for complex pattern recognition

Evaluation Metrics:

• Accuracy, Precision, Recall, F1-score
• Confusion matrices for detailed error analysis
• ROC curves and AUC scores

Infrastructure Planning

The deployment architecture will include:

• Training: AWS SageMaker for model training and hyperparameter tuning
• Storage: S3 for datasets and model artifacts
• Model Registry: SageMaker Model Registry for versioning
• Monitoring: CloudWatch for performance metrics and drift detection

Components

1. Data Pipeline

• Data ingestion from various sources
• Preprocessing and feature engineering
• Data validation and quality checks

2. Model Training

• Experiment tracking to compare different approaches
• Hyperparameter optimization using grid/random search
• Cross-validation for robust performance estimates

3. Deployment Infrastructure

• SageMaker endpoints for real-time inference
• Batch prediction for large-scale processing
• A/B testing framework for model comparison

4. Monitoring & Maintenance

• Performance monitoring dashboards
• Data drift detection
• Automated retraining triggers

Key Challenges & Solutions

Challenge: Handling imbalanced sentiment classes Solution: Using techniques like SMOTE, class weights, and stratified sampling

Challenge: Maintaining model performance over time Solution: Implementing monitoring and automated retraining pipelines

Challenge: Reproducibility across experiments Solution: Version control for data, code, and models using DVC and MLflow

What I’m Learning

• ML Workflow: Understanding the full lifecycle from problem definition to production
• Model Evaluation: Going beyond accuracy to understand model behavior deeply
• MLOps Practices: Versioning, monitoring, and automating ML workflows
• AWS Services: Hands-on experience with SageMaker and related services
• Engineering Mindset: Treating ML models as software products

Next Steps

1. Complete TensorFlow model training and evaluation
2. Set up experiment tracking with MLflow or SageMaker Experiments
3. Design deployment architecture on AWS SageMaker
4. Implement basic monitoring for deployed model
5. Document lessons learned and best practices
6. Explore advanced techniques like transfer learning and ensemble methods

Resources

This project draws from various learning resources:

• AWS SageMaker documentation and tutorials
• TensorFlow and scikit-learn documentation
• MLOps best practices from industry practitioners
• Academic papers on sentiment analysis techniques