Discovering public sentiment in social media for predicting stock movement of publicly listed companies
Bing Li, Chun Chung Chan, Carol Ou, Sun Ruifeng
Department of Computing
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.
https://doi.org/10.1016/j.is.2016.10.001
Parallel architecture
SMeDA-SA
Social media analysis
Stock prediction
10.1016/j.is.2016.10.001
Li, B., Chan, C. C., Ou, C., & Ruifeng, S. (2017). Discovering public sentiment in social media for predicting stock movement of publicly listed companies. Information Systems, 69, 81-92. https://doi.org/10.1016/j.is.2016.10.001
Li, Bing ; Chan, Chun Chung ; Ou, Carol ; Ruifeng, Sun. / Discovering public sentiment in social media for predicting stock movement of publicly listed companies. In: Information Systems. 2017 ; Vol. 69. pp. 81-92.
@article{f8c1c6649eca4d8a9bbe11e922f3a20c,
title = "Discovering public sentiment in social media for predicting stock movement of publicly listed companies",
abstract = "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.",
keywords = "Big data, Data mining, Parallel architecture, Sentiment analysis, SMeDA-SA, Social media analysis, Stock prediction, Twitter",
author = "Bing Li and Chan, {Chun Chung} and Carol Ou and Sun Ruifeng",
doi = "10.1016/j.is.2016.10.001",
journal = "Information Systems",
Li, B, Chan, CC, Ou, C & Ruifeng, S 2017, 'Discovering public sentiment in social media for predicting stock movement of publicly listed companies', Information Systems, vol. 69, pp. 81-92. https://doi.org/10.1016/j.is.2016.10.001
Discovering public sentiment in social media for predicting stock movement of publicly listed companies. / Li, Bing; Chan, Chun Chung; Ou, Carol; Ruifeng, Sun.
In: Information Systems, Vol. 69, 01.09.2017, p. 81-92.
T1 - Discovering public sentiment in social media for predicting stock movement of publicly listed companies
AU - Li, Bing
AU - Chan, Chun Chung
AU - Ou, Carol
AU - Ruifeng, Sun
N2 - 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.
AB - 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.
KW - Big data
KW - Data mining
KW - Parallel architecture
KW - SMeDA-SA
KW - Social media analysis
KW - Stock prediction
KW - Twitter
U2 - 10.1016/j.is.2016.10.001
DO - 10.1016/j.is.2016.10.001
JO - Information Systems
JF - Information Systems
Li B, Chan CC, Ou C, Ruifeng S. Discovering public sentiment in social media for predicting stock movement of publicly listed companies. Information Systems. 2017 Sep 1;69:81-92. https://doi.org/10.1016/j.is.2016.10.001