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

作者：

Highlights：

• 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-target is crucial.

摘要

•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-target is crucial.

论文关键词：News mining,News semantic analysis,Market sentiment analysis,Market prediction,FOREX prediction

论文评审过程：Available online 10 August 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.08.004