Posts Tagged ‘politics’

Trick or Treat: A Semantic Grab Bag Of Entertainment For the Occasion

Photo credit: Flickr/Erwiss, peace&love

It’s that spooky time of year again. With a happy Halloween to all, we present a selection of Halloween entertainment to dive into between answering the door for trick-or-treaters, or whenever you might like to have a little scarefest. They all come courtesy of searches done on some of the web’s semantically-enabled platforms.

Movies, from Jinni.com:

A search on Jinni, the semantic movie and TV “taste engine” that we first covered here, for “serial killer” theme, set in the 20th century in small towns, brings up some classics in the list of 41 that’s displayed, as well as some you may have missed when originally shown in theatres. Some in the list:

Halloween (of course): The 1978 John Carpenter-directed classic that started Jamie Lee Curtis on her fright-girl career (long before the yogurt days).  As a line in the summary says, the film “turned the slasher movie into a viable, successful genre. Halloween has been copied, parodied and even turned into a franchise of its own, but the original is still considered the best of the bunch.”

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Picking the President: Twindex, Twitris Track Social Media Electorate

The U.S. is a mere three months away from choosing who will be its leader for the next four years. The semantic web is a supporting player in the action.

This week, of course, saw the debut of the Twitter Political Index (Twindex), a joint effort between Twitter, Topsy, and the Mellman Group and NorthStar Opinion Research polling groups. Since the Semantic Web Blog last spoke with Topsy execs here, the company has refined its sentiment analysis to the point where it could be released for the Twindex. The sentiment analytics engine ingests hundreds of millions of English-language tweets a day and computes sentiment for all terms in Twitter, though that’s not publicly available yet.

In its Twindex incarnation, Topsy aggregates the underlying sentiment score minute by minute, and then that is rolled up into an hourly and daily score for each candidate, says Rishab Aiyer Ghosh, co-founder and chief scientist at Topsy Labs. Behind the scenes, “that score is normalized so that it is on 0 to 100 scale comparing to all the other terms people talk about,” he says, which is important for keeping perspective on the candidates in context relative to whatever else may be on the mind of the collective social media conscience. It also is weighted to include the scores of the previous two days before its publication at the end of the day, and smoothed out so that it doesn’t jump around in helter-skelter fashion.

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