Difference between revisions of "Gilbert et al., ICWSM 2010"
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* bagged complement Naive Bayes algorithm | * bagged complement Naive Bayes algorithm | ||
| − | After training on the data set, the author used a combination of these 2 classifiers to | + | After training on the data set, the author used a combination of these 2 classifiers to assess the anxiety level of the blogging community. |
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| + | To check accuracy, the author used Granger-causal Analysis to test whether anxiety level gave good information to movements on stock market. | ||
== Experimental result == | == Experimental result == | ||
Revision as of 23:23, 6 February 2011
Contents
Citation
Authors : Eric Gilbert, Karrie Karahalios
Title : Widespread Worry and the Stock Market
Conference : ICWSM 2010
Online version
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: [3]
