Improving prediction of stock market indices by analyzing the psychological states of twitter users

Alexander Porshnev (), Ilya Redkin () and Alexey Shevchenko ()
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Alexander Porshnev: National Research University Higher School of Economics, Social Science department, N.Novgorod, Russia
Ilya Redkin: National Research University Higher School of Economics, Business Informatics faculty, N.Novgorod, Russia
Alexey Shevchenko: National Research University Higher School of Economics, Business Informatics faculty, N.Novgorod, Russia

HSE Working papers from National Research University Higher School of Economics

Abstract: In our paper, we analyze the possibility of improving the prediction of stock market indicators by conducting a sentiment analysis of Twitter posts. We use a dictionary-based approach for sentiment analysis, which allows us to distinguish eight basic emotions in the tweets of users. We compare the results of applying the Support Vector Machine algorithm trained on three sets of data: historical data, historical and “Worry”, “Fear”, “Hope” words count data, historical data and data on the present eight categories of emotions. Our results suggest that the Twitter sentiment analysis data provides additional information and improves prediction as compared to a model based solely on information on previous shifts in stock indicators.

Keywords: stock market; forecast; Twitter; mood; psychological states; Support Vectors Machine; machine learning/ (search for similar items in EconPapers)
JEL-codes: G02 G17 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2013
New Economics Papers: this item is included in nep-for
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Citations: View citations in EconPapers (4)

Published in WP BRP Series: Financial Economics / FE, December 2013, pages 1-25

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