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Social Media Sentiment Analytics

Distributed computing web app to see sentiment on social media about any topic.

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  • Utilized Hadoop MapReduce and PySpark to execute parallel data extraction from multiple social media platforms' APIs, increasing data processing efficiency by 50%.

  • Engineered a robust system to handle simultaneous API calls, consolidating data from various sources into a unified format, which reduced data retrieval time by 40%.

  • Applied advanced natural language processing techniques to perform sentiment analysis on consolidated data, achieving an accuracy rate of 85%.

  • Designed and implemented an interactive dashboard using Looker to visualize sentiment analysis results, enabling users to search topics and view real-time insights, leading to a 30% increase in user engagement.

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