Real-time Sentiment Analysis

NLP Pipeline for Social Media Sentiment Classification

Python NLTK spaCy scikit-learn Twitter API React

Project Overview

A comprehensive Natural Language Processing pipeline that classifies social media sentiment in real-time. The system compares multiple machine learning approaches including Naive Bayes, Support Vector Machines, and Neural Networks, featuring a React-based frontend with live data streaming and an A/B testing framework.

Key Features

  • Real-time social media data streaming and processing
  • Multiple NLP models: Naive Bayes, SVM, Neural Networks
  • Advanced text preprocessing with NLTK and spaCy
  • Interactive React frontend with live sentiment visualization
  • A/B testing framework for model comparison
  • Twitter API integration for live data collection

Technical Implementation

NLP Pipeline
  • Text Preprocessing: Tokenization, lemmatization, stop word removal, emoji handling
  • Feature Extraction: TF-IDF vectorization, word embeddings, n-gram analysis
  • Model Training: Multiple classifiers with hyperparameter tuning
  • Real-time Classification: Streaming data processing with model inference
Architecture
  • Backend: Python Flask API with WebSocket support
  • Frontend: React with real-time data visualization (Chart.js)
  • Data Collection: Twitter API streaming with rate limit handling
  • Storage: MongoDB for historical data and analytics
  • ML Pipeline: scikit-learn pipelines with model versioning

Challenges & Solutions

Challenge: Handling Real-time Data Streams

Solution: Implemented asynchronous processing with WebSockets and message queues to handle high-volume data without blocking.

Challenge: Dealing with Sarcasm and Context

Solution: Enhanced feature engineering with context windows, emoji sentiment, and punctuation patterns.

Challenge: Model Performance vs Latency

Solution: Created a tiered system using fast models for real-time and complex models for batch processing.

What I Learned

  • Advanced NLP techniques and text preprocessing strategies
  • Real-time data processing and WebSocket implementation
  • Building full-stack ML applications with React and Flask
  • A/B testing methodologies for ML model evaluation
  • API integration and rate limiting strategies
  • Deployment considerations for ML systems in production

Project Links

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