Social Media Sentiment Analysis Tool

Scalable NLP pipeline processing 500k+ posts/hour using BERT and Apache Spark with 85% accuracy.

Overview

Developed a scalable NLP pipeline capable of analyzing over 500,000 social media posts per hour in real-time. The system leveraged transformer-based models and distributed computing to detect sentiment with high accuracy.

Approach

  • Data Collection: Twitter/Reddit APIs for live stream data.
  • Preprocessing: Tokenization, stopword removal, lemmatization, stemming.
  • Modeling: Fine-tuned BERT for sentiment polarity (positive/negative/neutral).
  • Scalability: Implemented distributed processing using Apache Spark.
  • Evaluation: Accuracy, precision, recall, F1-score, confusion matrix.

Results

  • Achieved 85% accuracy in real-time sentiment classification.
  • Successfully scaled to process 500k+ posts/hour.
  • Provided actionable insights for brands monitoring public opinion.

Skills demonstrated: transformer-based NLP (BERT), big data processing (Spark), distributed systems, real-time sentiment analysis.