Election 2016: Analyzing Real-Time Twitter Sentiment with SingleStore Pipelines

Twitter has become a significant platform for real-time discussions and analysis during the 2016 presidential election, with Pew Research Center stating that 44% of U.S. adults reported learning about the election from social media in January 2016. The first presidential debate between Donald Trump and Hillary Clinton was the most-tweeted event ever on Twitter, with 17.1 million interactions recorded. To analyze sentiment around these candidates, a real-time analytics demo was built using Apache Kafka, SingleStore, Machine Learning, and Pipelines. The demo collects tweets containing keywords related to Hillary and Trump, analyzes their sentiment, and provides insights into the trending positive or negative sentiments for each candidate in real-time. Two pipelines were created: one to store data from the collected tweets into a table and another to perform sentiment analysis using Python's Natural Language Toolkit (nltk) Vader module. The demo allows users to access analytics through SQL queries, visualizing rolling average tweet sentiment for both candidates in real-time, and can be accessed by following instructions on GitHub or downloading SingleStore Pipelines.

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