Supercomputer uses Twitter to tell how you are feeling

Twitter – 140 characters, craziness, text, micro-blogging. That’s right they all have become synonyms now. While the number of Twitter users increases every day globally, the number of tweets updated can reveal real time information around the globe. Global Twitter Heat map demonstrate the heat or volume of tweets sent to the Internet. Twitter sentiments and hashtag analysis has made information processing and analyzing to keep high on the next level.

US Sentiment as reflected in live Twitter Feed, courtesy of Silicon Graphics International
US Sentiment as reflected in live Twitter Feed, courtesy of Silicon Graphics International
Be it Hurricane Sandy or US Election or even the Euro Riots, or be it Aljazeera’s The Stream live TV show, Twitter sentiments and feelings are exposed, help determine what people really think and feel about the ongoing events around the world.

The latest heat map offers a real-time analysis of random data using supercomputers. One such is SGI UV 2000 Big Brain supercomputer at the University of Illinois which processes an impressive amount of raw information from the web very quickly and tells how everybody is feeling on Twitter right now. Much more than the Twitter sentiments.

The realtime Twitter tracking has been able to create more information regarding the people’s view, both positive and negative sentiments, prediction analysis, and much more. The Global Heartbeat Project, a partnership between supercomputer supplier SGI Kalev H. Leetaru of the University of Illinois and Dr. Shaowen Wang of the CyberInfrastructure and Geospatial Information (CIGI) Laboratory at the University of Illinois at Urbana-Champaign, also analyzed tweets during superstorm Sandy. Using red for negative tweets and blue for positive tweets regarding Sandy, the map reveals the mood in various areas of the U.S. as Sandy progressed up the East Coast.

The below video is all about The Global Twitter Heartbeat project performs real-time stream processing of ten percent of Twitter’s 400M daily tweets as they are posted. The project analyses every tweet to assign location (not just GPS-tagged tweets, but processing the text of the tweet itself), and tone values and then visualizes the conversation in a heat map infographic that combines and displays tweet location, intensity and tone. With SGI UV, the entire process from data analysis to heat map was produced once per second.

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