Analysis of Twitter Users’ Mood for Prediction of Gold and Silver Prices in the Stock Market

  • Alexander PorshnevEmail author
  • Ilya Redkin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 436)


The question about possibilities to use Twitter users’ moods to increase accuracy of stock price movement prediction draws attention of many researchers. In this paper we examine the possibility of analyzing Twitter users’ mood to improve accuracy of predictions for Gold and Silver stock market prices. We used a lexicon-based approach to categorize the mood of users expressed in Twitter posts and to analyze 755 million tweets downloaded from February 13, 2013 to September 29, 2013. As forecasting technique, we select Support Vector Machines (SVM), which have shown the best performance. Results of SVM application to prediction the stock market prices for Gold and Silver are discussed.


Prediction Gold Silver Twitter Mood Psychological states Support vector machines Behavioral finance 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsNizhny NovgorodRussia

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