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We propose and demonstrate an approach for the often attempted problem of market prediction. We restrict our study to a widely purchased and well recognized commodity, crude oil, which experiences significant volatility. Robust debate exists over the applicability of the weak and semi-strong versions of the Efficient Market Hypothesis (EMH) to financial markets. In this paper we train nine learners...
Traditional machine learning requires data to be described by attributes prior to applying a learning algorithm. In text classification tasks, many feature engineering methodologies have been proposed to extract meaningful features, however, no best practice approach has emerged. Traditional methods of feature engineering have inherent limitations due to loss of information and the limits of human...
Performing sentiment analysis of tweets by training a classifier is a challenging and complex task, requiring that the classifier can correctly and reliably identify the emotional polarity of a tweet. Poor data quality, due to class imbalance or mislabeled instances, may negatively impact classification performance. Ensemble learning techniques combine multiple models in an attempt to improve classification...
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