Networked Insights analytics engine Kairos processes unstructured data from millions of sites, blogs, and social platforms like Twitter and Tumblr. Billions of public posts are then analyzed and classified across 25,000 topics, emotions, and demographics—turning noisy social data into insights.
Source: Wired Magazine
Date: November 9th, 2016
1) “We built 4 metrics: Awareness, Positivity, Negativity and Intent, of which only Negativity and Intent proved to be valuable in predicting elections. Negativity and Intent are natural language processing classifiers which take advantage of sentence structure as well as keyword matching.” In what ways could a company use this approach to analyzing data?
2) “We typically look at a minimum of thousands of conversations or people to gather our assumptions on conversational themes.” You get access to the Twitter “firehose” pretty easily to feed into this sort of analytics engine. What things would you want to uncover from this?
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