Classifying Sentiments Based on BPSO Random Subspace
Zhang Qingqing1,2(), Liu Xilin2
1School of Management, Xi’an Polytechnic University, Xi’an 710048, China 2School of Management, Northwestern Polytechnical University, Xi’an 710129, China
[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.
张庆庆, 刘西林. 基于BPSO随机子空间的文本情感分类研究[J]. 数据分析与知识发现, 2017, 1(5): 71-81.
Zhang Qingqing,Liu Xilin. Classifying Sentiments Based on BPSO Random Subspace. Data Analysis and Knowledge Discovery, 2017, 1(5): 71-81.
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