Text mining for market prediction: A systematic review |
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| Affiliation: | 1. Department of Information Science, Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Research & Higher Degrees, Sunway University, No. 5, Jalan University, Bandar Sunway, 46150 Petaling Jaya, Selangor DE, Malaysia;1. Faculty of Engineering and Computer Science, Concordia University, Canada;2. Faculty of Computers and Information, Menofia University, Egypt;3. Department of Automatic Control and Systems Engineering, Sheffield University, UK;1. College of Computer Science and Technology, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou 310023, China;2. Division of Information Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore;1. Research and Higher Studies Center, National Polytechnic Institute, A.P. 14-740, 07000 Mexico City, Mexico;2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Batiz w/n and Miguel Othon de Mendizabal, P.O. 07738, Mexico City, Mexico |
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| Abstract: | The quality of the interpretation of the sentiment in the online buzz in the social media and the online news can determine the predictability of financial markets and cause huge gains or losses. That is why a number of researchers have turned their full attention to the different aspects of this problem lately. However, there is no well-rounded theoretical and technical framework for approaching the problem to the best of our knowledge. We believe the existing lack of such clarity on the topic is due to its interdisciplinary nature that involves at its core both behavioral-economic topics as well as artificial intelligence. We dive deeper into the interdisciplinary nature and contribute to the formation of a clear frame of discussion. We review the related works that are about market prediction based on online-text-mining and produce a picture of the generic components that they all have. We, furthermore, compare each system with the rest and identify their main differentiating factors. Our comparative analysis of the systems expands onto the theoretical and technical foundations behind each. This work should help the research community to structure this emerging field and identify the exact aspects which require further research and are of special significance. |
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| Keywords: | Online sentiment analysis Social media text mining News sentiment analysis FOREX market prediction Stock prediction based on news |
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