With the development of e-commerce, online shopping Web sites provide reviews for helping a customer to make the best choice. However, the number of reviews is huge, and the content of reviews is typically redundant and non-standard. Thus, it is difficult for users to go through all reviews in a short time and find the distinguishing characteristics of a product from the reviews. To resolve this problem, a method to mine top- k emerging patterns was proposed and applied to mining reviews of different products. Based on the proposed method, a prototype, called ReviewScope, was designed and implemented. ReviewScope can find significant comments of certain goods as decision basis, and provide visualization results. The case study on real world data set of JD.com demonstrates that ReviewScope is effective, flexible and user-friendly.

To solve the low classification accuracy and poor interpretability of selected features in traditional credit risk evaluation, a new model using Sparse Bayesian Learning (SBL) to evaluate personal credit risk (SBLCredit) was proposed in this paper. The SBLCredit utilized the advantages of SBL to get as sparse as possible solutions under the priori knowledge on the weight of features, which led to both good classification performance and effective feature selection. SBLCredit improved the classification accuracy of 4.52%, 6.40%, 6.26% and 2.27% averagely when compared with the state-of-the-art K-Nearest Neighbour (KNN), Nave Bayes, decision tree and support vector machine respectively on real-world German and Australian credit datasets. The experimental results demonstrate that the proposed SBLCredit is a promising method for credit risk evaluation with higher accuracy and fewer features.