Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment

•FOREX prediction through text mining of news is viable and effective.•Feature-selection by abstraction of word-hypernyms increases prediction accuracy.•Feature-weighting based on the sum of pos and neg sentiment scores is effective.•Feature-reduction based on maximum optimization for prediction-tar...

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Veröffentlicht in:	Expert systems with applications Jg. 42; H. 1; S. 306 - 324
Hauptverfasser:	Khadjeh Nassirtoussi, Arman, Aghabozorgi, Saeed, Ying Wah, Teh, Ngo, David Chek Ling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Ltd 01.01.2015 Elsevier
Schlagworte:	Algorithms Applied sciences Artificial intelligence Breaking Computer science; control theory; systems Decision theory. Utility theory Exact sciences and technology Exchange Expert systems FOREX prediction Information systems. Data bases Market prediction Market sentiment analysis Markets Memory organisation. Data processing Multilayers News mining News semantic analysis Operational research and scientific management Operational research. Management science Portfolio theory Semantics Software Speech and sound recognition and synthesis. Linguistics Texts Market prediction FOREX prediction News mining Market sentiment analysis News semantic analysis Noise reduction Layer model Data mining Modeling Relevance Motivation Semantics News Sparse matrix Selection criterion Dynamic model Exchange rate Textual data Data analysis Dimensionality Integration Filtration Knowledge representation Financial market Text Market survey Abstraction Noise control Data reduction Social decision Dimension reduction Monetary market Heuristic method Foreign currency Prediction market Algorithm analysis Press
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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