Traditional feature selection methods have great limitations in the imbalanced text sentiment tendency classification, which are mainly reflected in the high feature dimension, the sparse characteristics, and the imbalanced feature distribution, making the reduction of classification accuracy. According to the distribution of emotional features of imbalanced texts, a Three-Way Decisions-Feature Selection algorithm (TWD-FS) was proposed for imbalanced text sentiment classification based on three-way decisions. In order to reduce the number of feature words and reduce the feature dimension, two supervised feature selection methods were combined, and the feature words selected were further filtered in order to make them satisfy the characteristics of the maximum between-class scatter degree and the minimum within-class scatter degree. In addition, the imbalance of sentiment features was decreased and the classification accuracy of minority sentiment was effectively improved by combining positive and negative sentiment features. The experimental results on COAE2013 Chinese microblog imbalanced datasets and other datasets show that the proposed feature selection algorithm TWD-FS can effectively improve the accuracy of imbalanced text sentiment classification.

In order to obtain a better approximate approximation effect in multi-granulation rough set model for target conception, an intuitionistic fuzzy rough set and a multi-granulation rough set were combined together and a model of intuitionistic fuzzy multi-granulation rough set was proposed. Due to the loose defect of the target approximation of the model, a variable intuitionistic fuzzy multi-granulation rough set model was proposed by introducing parameters to improve the proposed model, and the validity of this model was proved. In addition, on the basis of this model, a corresponding approximate distribution reduction algorithm was also proposed. The simulation results show that, compared with the existing fuzzy multi-granulation decision-theoretic rough set and multi-granulation double-quantitative decision-theoretic rough set, the proposed lower approximation distribution reduction algorithm has 2 to 4 attributes more than that of them, and the proposed upper approximate distribution reduction algorithm has 1 to 5 attributes less than that of them; meanwhile, the approximation accuracy of reduction results is more reasonable and superior. Theoretical analysis and experimental results verify that the proposed variable intuitionistic fuzzy multi-granulation rough set model has higher superiority in terms of approximating approximation and reducing dimensions.