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This a [[Category::Paper]] discussed in Social Media Analysis 10-802 in Spring 2011. == Citation == Title : Widespread Worry and the Stock Market Authors : Eric Gilbert, Karrie Karahalios Conference : ICWSM 2010 == Online version == [http://social.cs.uiuc.edu/people/gilbert/pub/icwsm10.worry.gilbert.pdf Widespread Worry and the Stock Market] <br> [http://social.cs.uiuc.edu/people/gilbert/38 Website for the paper] == Summary == The stock market usually reflects business fundamentals, such as corporate earnings. However, we also see many events that seem rooted in human emotion more than anything else, from “irrational exuberance” during booms to panicked sell-offs during busts. In this paper, Gilbert et al used sentiment analysis methods to build an anxiety index for LiveJournal, an active blogging community. Then, they correlated their anxiety index to the S&P 500. == Brief description of the method == There are two main parts involved in predicting stock market movement base on anxiety * Boost Decision Tree * bagged complement Naive Bayes algorithm After training on the data set, the author used a combination of these 2 classifiers to assess the anxiety level of the blogging community. To check accuracy, the author used Granger-causal Analysis to test whether anxiety level gave good information to movements on stock market. == Experimental result == The resulted predictions are tested using Granger-causal analysis, which use econometric techniques to tell whether the Anxiety Index provides useful information for projecting future stock market prices not already contained in the market itself. Statistically, this paper showed that in general moods from an online community has novel predictive information about the stock market. == Dataset == LiveJournal post stream gathered using atom listener on: [http://atom.services.livejournal.com/atom-stream.xml LiveJournal atom stream] == Related Papers There has been a lot of work on using sentiment analysis to predict the stock market. * The paper by [[RelatedPaper::Chua, Milosavljevic and Curran]] Uses bayes classifiers to classify the sentiment of a internet stock message board.

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