TY - GEN
T1 - CashTagNN
T2 - 11th International Conference on Intelligent Systems: Theories and Applications, SITA 2016
AU - Rajesh, Neeraj
AU - Gandy, Lisa
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/5
Y1 - 2016/12/5
N2 - In this paper we discuss a system, CashTagNN, which uses the sentiment and subjectivity scores of tweets that include cashtags of two companies, Apple and Johnson and Johnson, to model stock market movement, and in particular predict opening and closing stock market prices. We demonstrate that by using only sentiment and subjectivity along with a neural network machine learning model we can predict the opening and closing prices of the two companies with high accuracy.
AB - In this paper we discuss a system, CashTagNN, which uses the sentiment and subjectivity scores of tweets that include cashtags of two companies, Apple and Johnson and Johnson, to model stock market movement, and in particular predict opening and closing stock market prices. We demonstrate that by using only sentiment and subjectivity along with a neural network machine learning model we can predict the opening and closing prices of the two companies with high accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85010384365&partnerID=8YFLogxK
U2 - 10.1109/SITA.2016.7772262
DO - 10.1109/SITA.2016.7772262
M3 - Conference contribution
AN - SCOPUS:85010384365
T3 - SITA 2016 - 11th International Conference on Intelligent Systems: Theories and Applications
BT - SITA 2016 - 11th International Conference on Intelligent Systems
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 October 2016 through 20 October 2016
ER -