Blog: Analyzing Almost 10 Million Tweets, Research Finds Public Mood Can Predict Dow Days in Advance

Analyzing Almost 10 Million Tweets, Research Finds Public Mood Can Predict Dow Days in Advance
IU News Room (10/18/10) Steve Chaplin

Indiana University researchers found that by analyzing millions of tweets they can predict the movement of the Dow Jones Industrial Average (DJIA) up to a week in advance with near 90 percent accuracy. Indiana professor Johan Bollen and Ph.D. candidate Huina Mao used two mood-tracking tools to analyze the text content of more than 9.8 million Twitter feeds and compared the public mood to the DJIA's closing value. One tool, called OpinionFinder, analyzed the tweets to give a positive or negative daily time series of public mood. The other tool, Google-Profile of Mood States, measures the mood of tweets in six dimensions--calm, alert, sure, vital, kind, and happy. The two tools gave the researchers seven public mood time series that could be matched against a similar daily time series for the DJIA closing. "What we found was an accuracy of 87.6 percent in predicting the daily up and down changes in the closing values of the Dow Jones Industrial Average," Bollen says. The researchers demonstrated that public mood can significantly improve the accuracy of the basic models currently used to predict Dow Jones closing values by implementing a prediction model called a Self-Organizing Fuzzy Neural Network.

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