%A Zhang Qingqing,Liu Xilin %T Classifying Sentiments Based on BPSO Random Subspace %0 Journal Article %D 2017 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.05.09 %P 71-81 %V 1 %N 5 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4368.shtml} %8 2017-05-25 %X

[Objective] This paper aims to solve the issue of representing high dimensional features in Chinese sentiment analysis, with the help of RS_BPSO, a selective ensemble algorithm. [Methods] First, we developed the framework and algorithm of the proposed RS_BPSO model based on the theory of Random Subspace and Binary Particle Optimization. Then, we transformed the Chinese review corpus into structured feature vectors and examined the new model. [Results] We found that the diversity and accuracy of the RS_BPSO model better than the standard RS model. [Limitations] We did not run the proposed model with corpus in foreign languages. [Conclusions] The RS_BPSO model could be an effective method to classify Chinese sentiments.