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Table 6 Summary of related work on the sentiment analysis for the same datasets

From: QER: a new feature selection method for sentiment analysis

Paper Dataset Baseline accuracy (%) Best accuracies observed (%) Classifier
[4] Movie 78.7   NB, SVM
[7] Movie   87.1 minimum cut SVM
[8] Movie
Product
79.9
74.3
85.7 CHI2; 86.9 DFD; 80.9 OCFS
73.7 CHI2; 75 DFD; 73.8 OCFS
MEM
[9] Movie
Product
84.2
80.9 Book; 78.9 DVD; 80.8 El
91.8
92.5 Book; 91.5 DVD; 91.8 El
mRMR with composite features
BNBM, SVM
[23] Product 70.1 84.2% Kitc. semantic orientation SVM
[24] Movie
Product
84.8
74.7 Book; 77.2 DVD; 80.8 El.; 83.3 Kitc
87.7
81.8 Book; 83.8 DVD; 85.9 El.; 88.7 Kitc word relation based method
NB, SVM, MEM
[25] Movie 84.1 92.7% Tabu search-enhanced Markov blanket model NB, SVM, MEM
Our study Movie
Product
84.8
76.2 Book; 78.4 DVD; 78.6 Elect; 81.4 Kitc
91.5 CHI2-IG; 87.1 DFD; 82.9 OCFS; 95.5 91.6 Book; 91.7 DVD; 88.8 Elect; 91.1 Kitc proposed QER NBM, SVM, MEM, DT