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Table 8 Result of the experiment using electronics dataset

From: Evaluating the performance of sentence level features and domain sensitive features of product reviews on supervised sentiment analysis tasks

Feature selection method ML algorithm BF Proposed feature
SLF SLF + DSF
Prec Rec F-meas Prec Rec F-meas Prec Rec F-meas
None Bayes Net 0.632 0.481 0.517 0.674 0.519 0.570 0.733 0.752 0.741
Naïve Bayes 0.595 0.595 0.595 0.674 0.519 0.570 0.678 0.495 0.549
Logistic 0.544 0.544 0.544 0.641 0.740 0.687 0.628 0.657 0.642
MLP 0.701 0.722 0.710 0.707 0.750 0.724 0.712 0.733 0.722
J48 0.607 0.658 0.629 0.651 0.798 0.717 0.743 0.733 0.738
Random Forest 0.660 0.747 0.670 *0.792 *0.817 *0.758 0.823 0.646 0.752
Random Tree 0.689 0.684 0.686 0.730 0.750 0.739 0.757 0.762 *0.760
CA Bayes Net 0.632 0.481 0.517 0.674 0.519 0.570 0.733 0.752 0.741
Naïve Bayes 0.595 0.595 0.595 0.674 0.519 0.570 0.678 0.495 0.549
Logistic 0.544 0.544 0.544 0.641 0.740 0.687 0.628 0.657 0.642
MLP 0.701 0.722 0.710 0.707 0.750 0.724 0.712 0.733 0.722
J48 0.607 0.658 0.629 0.651 0.798 0.717 0.743 0.733 0.738
Random Forest 0.660 0.747 0.670 *0.792 *0.817 *0.758 0.646 0.752 0.695
Random Tree 0.689 0.684 0.686 0.730 0.750 0.739 0.757 0.762 *0.760
GR Bayes Net 0.632 0.481 0.517 0.674 0.519 0.570 0.733 0.752 0.741
Naïve Bayes 0.595 0.595 0.595 0.674 0.519 0.570 0.678 0.495 0.549
Logistic 0.544 0.544 0.544 0.641 0.740 0.687 0.628 0.657 0.642
MLP 0.701 0.722 0.710 0.707 0.750 0.724 0.712 0.733 0.722
J48 0.607 0.658 0.629 0.651 0.798 0.717 0.743 0.733 0.738
Random Forest 0.660 0.747 0.670 0.792 0.817 0.758 0.646 0.752 0.695
Random Tree 0.689 0.684 0.686 0.730 0.750 0.739 0.757 0.762 *0.760
IG Bayes Net 0.632 0.481 0.517 0.674 0.519 0.570 0.733 0.752 0.741
Naïve Bayes 0.595 0.595 0.595 0.674 0.519 0.570 0.678 0.495 0.549
Logistic 0.544 0.544 0.544 0.641 0.740 0.687 0.628 0.657 0.642
MLP 0.701 0.722 0.710 0.707 0.750 0.724 0.712 0.733 0.722
J48 0.607 0.658 0.629 0.651 0.798 0.717 0.743 0.733 0.738
Random Forest 0.660 0.747 0.670 *0.792 *0.817 *0.758 0.646 0.752 0.695
Random Tree 0.689 0.684 0.686 0.730 0.750 0.739 0.757 0.762 *0.760
OneR Bayes Net 0.632 0.481 0.517 0.674 0.519 0.570 0.707 0.771 0.730
Naïve Bayes 0.595 0.595 0.595 0.674 0.519 0.570 0.652 0.790 0.715
Logistic 0.544 0.544 0.544 0.641 0.740 0.687 0.654 *0.800 0.720
MLP 0.701 0.722 0.710 0.707 0.750 0.724 0.712 0.733 0.722
J48 0.607 0.658 0.629 0.651 0.798 0.717 0.743 0.733 0.738
Random Forest 0.660 0.747 0.670 0.792 0.817 0.758 0.714 0.714 0.714
Random Tree 0.689 0.684 0.686 0.730 0.750 0.739 0.692 0.686 0.689
PCA Bayes Net 0.560 0.544 0.552 0.648 0.683 0.665 0.733 0.752 0.741
Naïve Bayes 0.590 0.620 0.604 0.648 0.683 0.665 0.679 0.619 0.645
Logistic 0.550 0.633 0.589 0.648 0.779 0.707 0.644 0.743 0.690
MLP 0.569 0.532 0.549 0.648 0.779 0.707 0.621 0.629 0.625
J48 0.633 0.620 0.627 0.651 0.798 0.717 0.652 0.790 0.715
Random Forest 0.564 0.696 0.623 0.649 0.788 0.712 0.648 0.762 0.700
Random Tree 0.653 0.684 0.666 0.720 0.731 0.725 0.683 0.629 0.652