Journal
2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW)
Volume -, Issue -, Pages 440-444Publisher
IEEE
DOI: 10.1109/ICDMW.2013.111
Keywords
Prediction; stock market indicator; Twitter; mood; psychological states; Support Vectors Machine; Neural Networks
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Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow us to evaluate presence of eight basic emotions in more than 755 million tweets. The application of Support Vectors Machine and Neural Networks algorithms to predict DJIA and S&P500 indicators are discussed.
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