Twitter Sentiment Analysis with Spark and Kafka

Social media platforms like Twitter generate vast volumes of data every second. Understanding public opinion during events, brand launches, or political movements is crucial for businesses, governments, and media houses. Traditional sentiment analysis pipelines cannot handle the velocity, volume, and variety of social data. Big data tools enable real-time collection, processing, and analysis of massive Twitter datasets to extract actionable sentiment insights.

Using Kafka for streaming tweets, Spark Streaming for real-time processing, and NLP models for classification, you can build a scalable sentiment analysis engine. Tweets are ingested in real-time, preprocessed (tokenization, stopword removal), vectorized, and classified into positive, negative, or neutral sentiments. Real-time dashboards then visualize trending topics, sentiment scores, and emotional swings across different locations or hashtags.

First, set up a Kafka producer that streams live tweets using the Twitter API, filtered by keywords or hashtags. Spark Streaming reads this data stream, applies preprocessing steps like tokenization and cleaning, and then uses an NLP classifier (like Logistic Regression, BERT, or LSTM) to predict the sentiment class. Sentiment counts are updated in real-time and visualized through dashboards, helping track public mood live.

• Use Twitter API (Tweepy, snscrape, or Twitter Developer Access) to stream tweets into a Kafka topic.
• Ingest streaming tweets into Spark Structured Streaming and perform real-time preprocessing like stopword removal and stemming.
• Apply trained NLP models (Logistic Regression, Naive Bayes, or deep learning models) to classify tweet sentiments live.
• Aggregate sentiment scores across different keywords, regions, or time intervals to spot trends.
• Visualize sentiments in real-time dashboards using Grafana, Streamlit, or custom web apps for impactful insights.

• Twitter Developer Portal
• Apache Spark Structured Streaming
• Sentiment Analysis with scikit-learn