Sentiment recognition of online course reviews using multi-swarm optimization-based selected features

Sentiment recognition of online course reviews is valuable to understand emotions and feelings of learners. Nowadays, an increasing number of course reviews are being generated with the emergence of Massive Open Online Courses (MOOCs), which offers teachers a chance to analyze the opinions of learners and improve teaching strategies. However, the unstructured data contain large amounts of redundant features, which will significantly impact the performance of machine learning. To select effective emotional features, we adopt a multi-swarm particle swarm optimization (MSPSO) method, which generates multi diverse particle swarms on several cross training subsets. These swarms are utilized to find the best features by the F-Measure fitness function. The experimental results on the real-life dataset show that MSPSO can effectively reduce redundancy of text features and capture discriminative features. Compared with conventional feature selection methods, MSPSO can gain the better performance when selecting the same dimensions. Besides, the result of a user survey indicates that 72.19% of subjects approve of the usability of the recognition results and effectiveness of the feature selection.