Open AccessTechnical Approach in Text Mining for Stock Market Prediction: A Systematic Review
Author(s) -
Mohammad Saiful Islam,
Imad Fakhri Taha Alshaikhli,
Rizal Mohd Nor,
Vijayakumar Varadarajan
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v10.i2.pp770-777
Subject(s) - computer science , stock market , data mining , rank (graph theory) , empirical research , sentiment analysis , curse of dimensionality , data science , field (mathematics) , task (project management) , information retrieval , machine learning , artificial intelligence , engineering , paleontology , philosophy , mathematics , systems engineering , epistemology , horse , combinatorics , pure mathematics , biology
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 soft-computing 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.