DATA MINING TWITTER TO PREDICT STOCK MARKET MOVEMENTS

Maxim Pecionchin () and Muhammad Usman
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Maxim Pecionchin: PhD candidate University of International Business and Economics, Beijing, China
Muhammad Usman: PhD candidate University of International Business and Economics, Beijing, China

Economy and Sociology, 2015, issue 1, 105-112

Abstract: In this paper we apply sentiment analysis of Twitter data from July through December, 2013 to find correlation between users’ sentiments and NASDAQ closing price and trading volume. Our analysis is based on the Affective Norms for English Words (ANEW). We propose a novel way of determining weighted mood level based on PageRank algorithm. We find that sentiment data is Granger-causal to financial market performance with high degree of significance. “Happy” and “sad” sentiment variables’ lags are strongly correlated with closing price and “excited” and “calm” lags are strongly correlated with trading volume.

Keywords: sentiment analysis; opinion mining; financial market; trading volume. (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:aat:journl:192

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