TY - JOUR
T1 - Detecting a Risk Signal in Stock Investment through Opinion Mining and Graph-Based Semi-Supervised Learning
AU - Yoon, Byungun
AU - Jeong, Yujin
AU - Kim, Sunhye
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - The objective of this study is to develop an algorithm to support a decision-making process in stock investment through opinion mining and graph-based semi-supervised learning. For this purpose, this research addresses the following core processes: (1) filtering fake information, (2) assessing credit risk and detecting risk signals, and (3) predicting future occurrences of credit events through sentiment analysis, word2vec, and graph-based semi-supervised learning. First, financial data, including news, texts in social network services, and financial statements, were collected. Among these data, fake information such as rumors and fake news was filtered by author analysis and a rule-based approach. Second, credit risk was assessed by opinion mining and sentiment analysis for both social data and news in the form of a sentiment score and the trend of documents for each stock. A signal for a credit event was then detected by the degree of assessed risk. Consequently, the possibility of credit events such as delisting and bankruptcy in the near future was forecast based on the risk signal using logistic regression. This research illustrated the real case of a company to validate the applicability of the proposed approach. The results of this study can help investors monitor a large amount of historically accumulated data and detect hidden signals of risk events ahead of time.
AB - The objective of this study is to develop an algorithm to support a decision-making process in stock investment through opinion mining and graph-based semi-supervised learning. For this purpose, this research addresses the following core processes: (1) filtering fake information, (2) assessing credit risk and detecting risk signals, and (3) predicting future occurrences of credit events through sentiment analysis, word2vec, and graph-based semi-supervised learning. First, financial data, including news, texts in social network services, and financial statements, were collected. Among these data, fake information such as rumors and fake news was filtered by author analysis and a rule-based approach. Second, credit risk was assessed by opinion mining and sentiment analysis for both social data and news in the form of a sentiment score and the trend of documents for each stock. A signal for a credit event was then detected by the degree of assessed risk. Consequently, the possibility of credit events such as delisting and bankruptcy in the near future was forecast based on the risk signal using logistic regression. This research illustrated the real case of a company to validate the applicability of the proposed approach. The results of this study can help investors monitor a large amount of historically accumulated data and detect hidden signals of risk events ahead of time.
KW - Decision support system
KW - early signal detection
KW - logistic regression
KW - opinion mining
KW - raph-based semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85091305517&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3021182
DO - 10.1109/ACCESS.2020.3021182
M3 - Article
AN - SCOPUS:85091305517
SN - 2169-3536
VL - 8
SP - 161943
EP - 161957
JO - IEEE Access
JF - IEEE Access
M1 - 9184877
ER -