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Song Lyrics, Twitter Help Chart Public Mood

July 20, 2011 841

Michael Haederle describes a new research method to work out the mood of a group of people.

Social scientists seeking to assess the collective mood of large groups of people traditionally have relied on slow, laborious sampling methods that usually entail some form of self-reporting.

Peter Dodds and Chris Danforth, mathematicians at the University of Vermont, dreamed up an ingenious way to sample the feelings of many more people much more quickly.

They downloaded the lyrics to 232,000 popular songs composed between 1960 and 2007 and calculated how often emotion-laden words like “love,” “hate,” “pain” and “baby” occurred in each.

Then they graphed their results, averaging over the emotional valence of individual words. A clearly negative trend emerged over the 47-year period, from bright and happy (think Pat Boone) to dark and depressive (death metal and industrial music come to mind).

The pair has used similar methods to analyze millions of sentences downloaded from blogs, as well as the text of every U.S. State of the Union address and a vast trove of Twitter tweets.

They see distinctive patterns emerging in how collective moods shift over time. The Internet, with its ability to transmit vast amounts of data, is the key…

Click here to read the entire article in Miller McCune Magazine.

One of Library Journal’s Best Magazines of 2008, Miller-McCune not only identifies policy issues of global important but provides evidence-based solutions offered by academic research and real-world models. Through excellent but understandable writing and proven judgment in what to cover, the nonprofit Miller-McCune has received a surprising amount of acclaim and, more importantly, a large and growing audience interested in the social and natural sciences.

View all posts by Pacific-Standard Magazine

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