Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75972
Title: Discovering public sentiment in social media for predicting stock movement of publicly listed companies
Authors: Li, B 
Chan, KCC 
Ou, C
Sun, RF 
Keywords: Social media analysis
Twitter
Stock prediction
Data mining
Sentiment analysis
Big data
SMeDA-SA
Parallel architecture
Issue Date: 2017
Publisher: Pergamon Press
Source: Information systems, 2017, v. 69, p. 81-92 How to cite?
Journal: Information systems 
Abstract: The popularity of many social media sites has prompted both academic and practical research on the possibility of mining social media data for the analysis of public sentiment. Studies have suggested that public emotions shown through Twitter could be well correlated with the Dow Jones Industrial Average. However, it remains unclear how public sentiment, as reflected on social media, can be used to predict stock price movement of a particular publicly-listed company. In this study, we attempt to fill this research void by proposing a technique, called SMeDA-SA, to mine Twitter data for sentiment analysis and then predict the stock movement of specific listed companies. For the purpose of experimentation, we collected 200 million tweets that mentioned one or more of 30 companies that, were listed in NASDAQ or the New York Stock Exchange. SMeDA-SA performs its task by first extracting ambiguous textual messages from these tweets to create a list of words that reflects public sentiment. SMeDA-SA then made use of a data mining algorithm to expand the word list by adding emotional phrases so as to better classify sentiments in the tweets. With SMeDA-SA, we discover that the stock movement of many companies can be predicted rather accurately with an average accuracy over 70%. This paper describes how SMeDA-SA can be used to mine social media date for sentiments. It also presents the key implications of our study.
URI: http://hdl.handle.net/10397/75972
ISSN: 0306-4379
EISSN: 1873-6076
DOI: 10.1016/j.is.2016.10.001
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