Preprocessing Technique for Classification of M-Learning Reviews using Soft Computing Approach

The development of communication technology has resulted in easy information access via internet. The rapid increase in the use of mobile devices has popularized pedagogical methods like learning through mobile devices, PDAs, etc. Various Mobile Learning (M-Learning) systems are available and the user opinions about them are aired in social blogs or review websites. This research paper investigates Opinion mining classifications particularly of M-Learning system based not only on words but also on the corpus from the reviewed documents. A preprocessing methodology is proposed in this paper to enhance classifications in the dataset under study. The corpus is ranked using SVD through which, the data is prepared for Opinion mining. The classification accuracy is evaluated through Naïve Bayes, Random Forest, k Nearest Neighbor (kNN) data mining algorithms and Learning Vector Quantization (LVQ), Elman Neural Network, Feed Forward Neural Network (FFNN) algorithms with the preprocessed dataset.