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Mining Mood Swings on the Real-Time Web


Sentiment toward browsers

This widget shows current sentiment toward competing Web browsers.

Viralheat

Many companies are turning to social-media sites to gauge the success of a new product and service. The latest activity on Facebook, Twitter, and countless other sites can reveal the public's current mood toward a new film, gadget, or celebrity, and analytics services are springing up to help companies keep track. Social-media analytics startup Viralheat, based in San Jose, CA, is now offering free, real-time access to the data it is collecting on attitudes toward particular topics or products. One of the first customers for this new service—called Social Trends—is ESPN, which plans to use Social Trends to show live popularity rankings for different NFL teams.

Viralheat uses natural-language processing and machine learning to sift through Twitter, Facebook fan pages, viral video sites, and Google Buzz posts to determine the Web's collective sentiment toward everything from popular browsers to Pepsi to Steve Jobs. The company sells its data and analytics service for a monthly fee, but CEO Raj Kadam says that Social Trends will provide a free way to people to access data the company is already collecting. When a paying customer asks Viralheat to track a particular term, they have the option to share that information publicly. Kadam says that about 70 percent of users agree to share this information.

Social Trends uses this information to provide a widget that can be embedded on a blog or website showing the sentiment around particular terms. These widgets stay connected to Viralheat's data stores through an application programming interface and are updated as the company collects more information. Viralheat believes the tool will be particularly useful for news sites wanting up-to-date infographics and for bloggers who want to track trends.

From Technology Review
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