Ensemble Classification Approach for Opinion Mining in Social Media and Online Newsfeeds for Forex Market Prediction

Opinions hindered in social media, and online news feeds to control the predictability of financial markets such as Forex and cause huge profits and losses for the investors. Due to this reason, financial market prediction through opinion mining has gained significant attention from researchers recently. Current approaches relied more on basic text mining algorithms and overlooked the knowledge generated through the integration of the multiple data sources. To overcome this deficiency, this study harnesses the knowledge required for decisive currency-pair prediction by analyzing forex news and social media data using ensemble classification. Three classification approaches namely Support Vector Machine (SVM), Naïve Bayes and K-Nearest Neighbor (KNN) are used in the ensemble classifier. Based on the classifier, impactful posts/comments are discovered to facilitate forex market prediction.