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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (5): 71-81    DOI: 10.11925/infotech.2096-3467.2017.05.09
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Classifying Sentiments Based on BPSO Random Subspace
Qingqing Zhang1,2(),Xilin Liu2
1School of Management, Xi’an Polytechnic University, Xi’an 710048, China
2School of Management, Northwestern Polytechnical University, Xi’an 710129, China
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[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.

Key wordsRandom Subspace      BPSO      Text Sentiment Classification      Subspace Rate     
Received: 28 March 2017      Published: 06 June 2017

Cite this article:

Qingqing Zhang,Xilin Liu. Classifying Sentiments Based on BPSO Random Subspace. Data Analysis and Knowledge Discovery, 2017, 1(5): 71-81.

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