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Twitter Mood Predicts the Stock Market

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Journal of Computational Science 2 (2011) 1–8

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Journal of Computational Science journal homepage: www.elsevier.com/locate/jocs

Twitter mood predicts the stock market
Johan Bollen a,∗,1 , Huina Mao a,1 , Xiaojun Zeng b a b

School of Informatics and Computing, Indiana University, 919 E. 10th Street, Bloomington, IN 47408, United States
School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester M13 9PL, United Kingdom

a r t i c l e

i n f o

Article history:
Received 15 October 2010
Received in revised form 2 December 2010
Accepted 5 December 2010
Available online 2 February 2011
Keywords:
Social networks
Sentiment tracking
Stock market
Collective mood

a b s t r a c t
Behavioral economics tells us that emotions can profoundly affect individual behavior and decisionmaking. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from largescale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public’s response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured

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