Title :
Machine Learning in Prediction of Stock Market Indicators Based on Historical Data and Data from Twitter Sentiment Analysis
Author :
Porshnev, Alexander ; Redkin, Ilya ; Shevchenko, Andrey
Author_Institution :
Higher Sch. of Econ., Nat. Res. Univ., Nizhny Novgorod, Russia
Abstract :
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.
Keywords :
Internet; data handling; learning (artificial intelligence); neural nets; social networking (online); stock markets; Twitter sentiment analysis; Twitter users; historical data; lexicon based approach; linguistic technologies; machine learning; market indicators; neural networks algorithms; psychological states; social media; stock market indicators; support vectors machine; Accuracy; Algorithm design and analysis; Dictionaries; Prediction algorithms; Stock markets; Support vector machines; Twitter; Neural Networks; Support Vectors Machine; Twitter; mood; prediction; psychological states; stock market indicators;
Conference_Titel :
Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4799-3143-9
DOI :
10.1109/ICDMW.2013.111
