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Author published in"Computers in Human Behavior"affiliate to

College of Management

Chien-Hsiang Liao

Department of Information Management,

Fu Jen Catholic University, New Taipei City, Taiwan

Article published in 

"Computers in Human Behavior" Volume 101, December 2019, Pages 402-408


Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach

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. [Full article]

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Keywords: Emantic analysis, Stock market trend analysis,Time series, Long short-term memory (LSTM)

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