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


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Alexander Porshnev , Ilya Redkin

ABSTRACT

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. More... »

PAGES

190-197

References to SciGraph publications

Book

TITLE

Analysis of Images, Social Networks and Texts

ISBN

978-3-319-12579-4
978-3-319-12580-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-12580-0_19

DOI

http://dx.doi.org/10.1007/978-3-319-12580-0_19

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029774624


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