基于时空多粒度的序贯三支情感分析

大数据时代下传统静态的情感分析方法已无法适应当前动态数据的量级和复杂度.为了改善传统的二支静态决策的不足,文中基于序贯三支决策思想提出基于时空多粒度的序贯三支情感分析方法.利用随时间增加的数据和拟合度较高的特征空间,构造具有时空特性的多层粒结构,平衡误分类代价和训练代价.使用3种基准分类器实际测试方法效率,在2个数据集上的实验表明,文中方法在维持分类质量的前提下,大幅减少分类代价.

Sequential Three-Way Sentiment Analysis Based on Temporal-Spatial Multi-granularity

The traditional static methods of sentiment analysis cannot meet the quantity and complexity requirements of dynamic data in the big data era. Therefore, grounded on the concept of sequential three-way decisions, a sequential three-way sentiment analysis framework based on temporal-spatial multi-granularity is proposed to overcome the shortcomings of the traditional two-way decisions. Firstly, a multi-layer granular structure with temporal-spatial features is constructed using increasing data and better-fitting feature space over time to balance the misclassification cost and training cost. Then, three typical sentiment classification methods are applied as benchmarks to test the efficiency of the proposed method. Finally, compared with the static methods, experimental results on two datasets show that the proposed method greatly reduces the classification costs with the classification quality maintained.