TY - JOUR
T1 - Modeling public mood and emotion
T2 - Stock market trend prediction with anticipatory computing approach
AU - Chen, Mu Yen
AU - Liao, Chien Hsiang
AU - Hsieh, Ren Pao
N1 - Funding Information:
The authors wish to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research under Contract No. MOST106-2634-F-025-001 , MOST106-2410-H-030-018-MY3 , and MOST105-2410-H-025-015-MY2 .
Funding Information:
The authors wish to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research under Contract No. MOST106-2634-F-025-001, MOST106-2410-H-030-018-MY3, and MOST105-2410-H-025-015-MY2.
Publisher Copyright:
© 2019
PY - 2019/12
Y1 - 2019/12
N2 - The science and technology is more and more developed. Digital media such as articles, commentary, videos, animations and others on the Internet is becoming more and more important. English semantic analysis has many basic technologies, many applications are also gradually budding in this basic technology. On the other hand, there is no uniform or complete reorganization of the basic technologies in Chinese semantic analysis. Chinese semantic analysis is difficult than English semantic analysis because it is difficult to judge the true meaning of Chinese words and sentences. This study collects articles about common news sites in Taiwan and related to individual stocks. After the data is preprocessed and Skip-gram, each word is converted to word features using Word2Vec. The Lexicon stores the most relevant words around the keyword. In the prediction stage, this study calculates the impact of new articles on the stock price according to the full training lexicon. Finally, this study uses the deep learning approach - LSTM (Long Short-Term Memory) to evaluate the final results. The aim of this study is to adopt anticipatory computing to explore the public mood and emotion from news articles. Then this study can predict the future stock market trend and can be the reference model to the related industries.
AB - The science and technology is more and more developed. Digital media such as articles, commentary, videos, animations and others on the Internet is becoming more and more important. English semantic analysis has many basic technologies, many applications are also gradually budding in this basic technology. On the other hand, there is no uniform or complete reorganization of the basic technologies in Chinese semantic analysis. Chinese semantic analysis is difficult than English semantic analysis because it is difficult to judge the true meaning of Chinese words and sentences. This study collects articles about common news sites in Taiwan and related to individual stocks. After the data is preprocessed and Skip-gram, each word is converted to word features using Word2Vec. The Lexicon stores the most relevant words around the keyword. In the prediction stage, this study calculates the impact of new articles on the stock price according to the full training lexicon. Finally, this study uses the deep learning approach - LSTM (Long Short-Term Memory) to evaluate the final results. The aim of this study is to adopt anticipatory computing to explore the public mood and emotion from news articles. Then this study can predict the future stock market trend and can be the reference model to the related industries.
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U2 - 10.1016/j.chb.2019.03.021
DO - 10.1016/j.chb.2019.03.021
M3 - Article
AN - SCOPUS:85072154300
SN - 0747-5632
VL - 101
SP - 402
EP - 408
JO - Computers in Human Behavior
JF - Computers in Human Behavior
ER -