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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
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
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Abstract  

[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 words： Random Subspace      BPSO      Text Sentiment Classification      Subspace Rate     
Received: 28 March 2017      Published: 06 June 2017
ZTFLH:  TP391.1  
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Qingqing Zhang
Xilin Liu

Cite this article:

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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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.05.09     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I5/71

数据集 三元组依存关系
酒店 140 911
图书 66 297
笔记本电脑 28 932
质量法 公式 编号
Q统计 ${{Q}_{ij}}=\frac{{{N}^{11}}{{N}^{00}}-{{N}^{10}}{{N}^{01}}}{{{N}^{11}}{{N}^{00}}+{{N}^{10}}{{N}^{01}}}$ (6)
相关系数$\rho $ ${{\rho }_{ij}}=\frac{{{N}^{11}}{{N}^{00}}-{{N}^{10}}{{N}^{01}}}{\sqrt{({{N}^{11}}+{{N}^{10}})({{N}^{01}}+{{N}^{00}})({{N}^{11}}+{{N}^{01}})({{N}^{10}}+{{N}^{00}})}}$ (7)
不一致度量dis $di{{s}_{ij}}=({{N}^{10}}+{{N}^{01}})/N$ (8)
双次失败度量DF $D{{F}_{ij}}=\frac{{{N}^{00}}}{N}$ (9)
k 酒店 图书 笔记本电脑
k=0.01 1 409 663 289
k=0.02 2 818 1 326 579
k=0.03 4 227 1 989 868
k=0.05 7 046 3 315 1 447
总个数 140 911 66 297 28 932
k RS RS_BPSO
0.01 0.6825 0.8342(17)
0.02 0.7183 0.8013(14)
0.03 0.7717 0.8293(13)
0.05 0.8075 0.8429(19)
k RS RS_BPSO
0.01 0.6867 0.8270(19)
0.02 0.7033 0.8434(19)
0.03 0.7633 0.8208(20)
0.05 0.785 0.8325(21)
k RS RS_BPSO
0.01 0.7867 0.8517(24)
0.02 0.8267 0.8762(29)
0.03 0.8067 0.8717(28)
0.05 0.8233 0.8634(22)
k DF dis Q统计 相关系数$\rho $
RS RS_BPSO RS RS_BPSO RS RS_BPSO RS RS_BPSO
0.01 0.3668 0.3715 0.4378 0.466 0.1507 0.0127 0.0972 0.0263
0.02 0.4396 0.4437 0.3759 0.4153 0.3794 0.1699 0.1958 0.0864
0.03 0.4677 0.4862 0.3718 0.379 0.3612 0.2837 0.179 0.136
0.05 0.5289 0.5452 0.333 0.3266 0.4448 0.4434 0.2144 0.2099
k DF dis Q统计 相关系数$\rho $
RS RS_BPSO RS RS_BPSO RS RS_BPSO RS RS_BPSO
0.01 0.321 0.3174 0.4701 0.4963 0.0667 -0.0321 0.048 -0.0099
0.02 0.3751 0.3834 0.4383 0.4585 0.1594 0.0477 0.0903 0.0351
0.03 0.4094 0.4079 0.409 0.44 0.2615 0.1071 0.1368 0.0589
0.05 0.4543 0.4576 0.3895 0.4115 0.2935 0.1663 0.1448 0.079
k DF dis Q统计 相关系数$\rho $
RS RS_BPSO RS RS_BPSO RS RS_BPSO RS RS_BPSO
0.01 0.3284 0.3271 0.4722 0.4986 0.0422 -0.0616 0.0399 -0.021
0.02 0.3753 0.3796 0.4559 0.4629 0.0482 0.0233 0.061 0.0265
0.03 0.4114 0.4073 0.428 0.441 0.1462 0.077 0.0875 0.057
0.05 0.4731 0.4764 0.3879 0.3909 0.2504 0.2225 0.1276 0.1146
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