Probabilistic Soft Logic (PSL), as a kind of declarative rule-based probability model, has strong extensibility and multi-domain adaptability. So far, it requires a lot of common sense and domain knowledge as preconditions for rule establishment. The acquisition of these knowledge is often very expensive and the incorrect information contained therein may reduce the correctness of reasoning. In order to alleviate this dilemma, the C5.0 algorithm and probabilistic soft logic were combined to make the data and knowledge drive the reasoning model together, and a semi-automatic learning method was proposed. C5.0 algorithm was used to extract rules, and artificial rules and optimized adjusted rules were supplemented as improved probabilistic soft logic input. The experimental results show that the proposed method has higher accuracy than the C5.0 algorithm and the PSL without rule learning on student performance prediction. Compared with the past method with pure hand-defined rules, the proposed method can significantly reduce the manual costs. Compared with Bayesian Network (BN), Support Vector Machine (SVM) and other algorithms, the proposed method also shows good results.

The traditional sentiment model faces two main problems in analyzing user's emotion of product reviews: 1) the lack of fine-grained emotion analysis for product attributes; 2) the number of product attributes shall be defined in advance. In order to alleviate the problems mentioned above, a fine-grained model for product attributes named User Sentiment Model (USM) was proposed. Firstly, the entities were clustered in product attributes by Hierarchical Dirichlet Processes (HDP) and the number of product attributes could be obtained automatically. Then, the combination of the entity weight in product attributes, the evaluation phrase of product attributes and sentiment lexicon was considered as prior. Finally, Latent Dirichlet Allocation (LDA) was used to classify the emotion of product attributes. The experimental results show that the model achieves a high accuracy in sentiment classification and the average accuracy rate of sentiment classification is 87%. Compared with the traditional sentiment model, the proposed model obtains higher accuracy on extracting product attributes as well as sentiment classification of evaluation phrases.