Seed Based Method for Identifying Efficient Online Reviews using Micro-reviews.

Review selection based on supervised learning model is utilized to classify the review based on diversification mechanism. Many contributions can be made on the proposed work in order to improve efficiency of the system; initially unsupervised classification technique can be proposed to cluster the content which is outlier. The class imbalance problem is defined in terms of which the ratio of the majority and minority class cardinalities which is considered as outlier. The main idea is to severely under sample the majority class thus creating a large number of distinct training sets. The outlier of data can be expressed as the data which is inefficient to cluster in either of the classes generated; hence these problems can be resolved using the random sampling technique which improves the performance of the system in terms of classification rate and reduction in the outlier data.