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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
ISBN: 978-1-5090-2847-4
pp: 1127-1134
Min-Yuh Day , Department of Information Management, Tamkang University, Taiwan
Chia-Chou Lee , Department of Information Management, Tamkang University, Taiwan
ABSTRACT
Investors have always been interested in stock price forecasting. Since the development of electronic media, hundreds pieces of financial news are released on different media every day. Numerous studies have attempted to examine whether the stock price forecasting through text mining technology and machine learning could lead to abnormal returns. However, few of them involved the discussion on whether using different media could affect forecasting results. Financial sentiment analysis is an important research area of financial technology (FinTech). This research focuses on investigating the influence of using different financial resources to investment and how to improve the accuracy of forecasting through deep learning. The experimental result shows various financial resources have significantly different effects to investors and their investments, while the accuracy of news categorization could be improved through deep learning.
INDEX TERMS
Machine learning, Media, Text mining, Share prices, Investment, Forecasting
CITATION

M. Day and C. Lee, "Deep learning for financial sentiment analysis on finance news providers," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 1127-1134.
doi:10.1109/ASONAM.2016.7752381
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