TY - GEN
T1 - Sentimental analysis of Chinese new social media for stock market information
AU - Chen, Guanhang
AU - He, Lilin
AU - Papangelis, Konstantinos
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/26
Y1 - 2019/8/26
N2 - The popularity of social media provides a new platform to collect big social data. With the development of social sentiment analysis, high business value extracted from social data are applied to various fields. Asset price prediction, as an emerging topic based on the behavioral economics, is closely linked to social data analysis. This research aims to explore the effort of sentiment analysis data in the prediction of China composite index. Data from Sina Weibo and financial community is processed to get the useful sentiment information. A linear regression model and a multilayer neural network algorithm are used to prove the relationship between social data and price market prediction. The experiments show a strong relationship between the numbers of negative sentiment and a multilayer perceptron model is effectively built to predict the composite index.
AB - The popularity of social media provides a new platform to collect big social data. With the development of social sentiment analysis, high business value extracted from social data are applied to various fields. Asset price prediction, as an emerging topic based on the behavioral economics, is closely linked to social data analysis. This research aims to explore the effort of sentiment analysis data in the prediction of China composite index. Data from Sina Weibo and financial community is processed to get the useful sentiment information. A linear regression model and a multilayer neural network algorithm are used to prove the relationship between social data and price market prediction. The experiments show a strong relationship between the numbers of negative sentiment and a multilayer perceptron model is effectively built to predict the composite index.
KW - Sentiment analysis
KW - stock market
UR - http://www.scopus.com/inward/record.url?scp=85074748819&partnerID=8YFLogxK
U2 - 10.1145/3357777.3357778
DO - 10.1145/3357777.3357778
M3 - Conference Proceeding
AN - SCOPUS:85074748819
T3 - ACM International Conference Proceeding Series
SP - 1
EP - 6
BT - PRAI 2019 - Proceedings of 2019 International Conference on Pattern Recognition and Artificial Intelligence
PB - Association for Computing Machinery
T2 - 2019 International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2019
Y2 - 26 August 2019 through 28 August 2019
ER -