An empirical comparison of supervised learning algorithms and hybrid WDBN algorithm for MOOC courses

In today’s scenario, sentiment classification has been acknowledged as a significant aspect, as it delivers the techniques for automatically analyzing people’s reviews to extract useful information on a product or service. Polarity classification is one among those techniques which determine the text polarity in the opinion. In this regard, the given paper explains a method for sentiment classification of online course reviews using a novel classifier, whale-based deep belief network (WDBN). The input course review data is pre-processed in the given technique, and the key features are extracted from the data using emotion-SentiWordNet based feature extraction process. To classify sentiments in the feature extracted data, WDBN is brought in by combining deep belief networks and whale optimization algorithm such that the weights of the network layers are selected optimally. The given technique, with the application of WDBN, classifies the course reviews into two classes, such as positive and negative class reviews. The given WDBN classifier is tested with the help of a publicly accessible online course review dataset, and the performance of the classifier is assessed using three metrics, such as sensitivity, specificity, and accuracy, where it could attain maximum performance of 86.3% sensitivity, 81.1% specificity and 86% accuracy.

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