Technical approach in text mining for stock market prediction: a systematic review

Islam, Mohammad Rabiul and Alshaikhli, Imad Fakhri Taha and Mohd. Nor, Rizal and Varadarajan, Vijayakumar (2018) Technical approach in text mining for stock market prediction: a systematic review. Indonesian Journal of Electrical Engineering and Computer Science, 10 (2). pp. 770-777. ISSN 2502-4752

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Abstract

Text mining methods and techniques have disclosed the mining task throughout information retrieval discipline in the field of soft computing techniques. To find the meaningful information from the vast amount of electronic textual data become a humongous task for trading decision. This empirical research of text mining role on financial text analysing in where stock predictive model need to improve based on rank search method. The review of this paper basically focused on text mining techniques, methods and principle component analysis that help reduce the dimensionality within the characteristics and optimal features. Moreover, most sophisticated softcomputing methods and techniques are reviewed in terms of analysis, comparison and evaluation for its performance based on electronic textual data. Due to research significance, this empirical research also highlights the limitation of different strategies and methods on exact aspects of theoretical framework for enhancing of performance.

Item Type:	Article (Journal)
Additional Information:	6534/63249
Uncontrolled Keywords:	Online news mining, Stock market prediction, Text mining approach
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Professor Imad Taha
Date Deposited:	25 Apr 2018 15:39
Last Modified:	25 Apr 2018 15:39
URI:	http://irep.iium.edu.my/id/eprint/63249

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