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Table 9 Result of the experiment using automobile 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 0.664 0.664 0.664 0.764 0.759 0.762 0.786 0.818 0.800
Naïve Bayes 0.701 0.770 0.735 0.764 0.759 0.762 0.786 0.818 0.800
Logistic 0.700 0.752 0.724 0.751 0.796 0.772 0.779 0.847 0.801
MLP 0.739 0.796 0.761 0.782 0.810 0.795 0.770 0.810 0.788
J48 0.681 0.761 0.719 *0.796 *0.847 *0.811 0.779 0.847 0.801
Random Forest 0.689 0.814 0.747 0.740 0.847 0.790 0.740 0.847 0.790
Random Tree 0.736 0.708 0.721 0.773 0.788 0.781 0.776 0.766 0.771
CA Bayes Net 0.707 0.770 0.735 0.764 0.759 0.762 0.786 0.818 0.800
Naïve Bayes 0.664 0.664 0.664 0.764 0.759 0.762 0.786 0.818 0.800
Logistic 0.700 0.752 0.724 0.751 0.796 0.772 0.779 0.847 0.801
MLP 0.739 0.796 0.761 0.782 0.810 0.795 0.770 0.810 0.788
J48 0.681 0.761 0.719 *0.796 *0.847 0.811 0.779 0.847 0.801
Random Forest 0.689 0.814 0.747 0.740 *0.847 0.790 0.740 0.847 0.790
Random Tree 0.736 0.708 0.721 0.773 0.788 0.781 0.776 0.766 0.771
GR Bayes Net 0.707 0.770 0.735 0.764 0.759 0.762 0.786 0.818 0.800
Naïve Bayes 0.664 0.664 0.664 0.764 0.759 0.762 0.786 0.818 0.800
Logistic 0.700 0.752 0.724 0.751 0.796 0.772 0.779 0.847 0.801
MLP 0.739 0.796 0.761 0.782 0.810 0.795 0.770 0.81 0.788
J48 0.681 0.761 0.719 *0.796 *0.847 *0.811 0.779 0.847 0.801
Random Forest 0.689 0.814 0.747 0.740 *0.847 0.790 0.740 0.847 0.790
Random Tree 0.736 0.708 0.721 0.773 0.788 0.781 0.776 0.766 0.771
IG Bayes Net 0.707 0.770 0.735 0.764 0.759 0.762 0.786 0.818 0.800
Naïve Bayes 0.664 0.664 0.664 0.764 0.759 0.762 0.786 0.818 0.800
Logistic 0.700 0.752 0.724 0.751 0.796 0.772 0.779 0.847 0.801
MLP 0.739 0.796 0.761 0.782 0.810 0.795 0.770 0.810 0.788
J48 0.681 0.761 0.719 *0.796 *0.847 *0.811 0.779 0.847 0.801
Random Forest 0.689 0.814 0.747 0.740 *0.847 0.790 0.740 0.847 0.790
Random Tree 0.736 0.708 0.721 0.773 0.788 0.781 0.776 0.766 0.771
OneR Bayes Net 0.664 0.664 0.664 0.764 0.759 0.762 0.786 0.818 0.800
Naïve Bayes 0.728 0.717 0.722 0.764 0.759 0.762 0.786 0.818 0.800
Logistic 0.700 0.752 0.724 0.751 0.796 0.772 0.779 0.847 0.801
MLP 0.739 0.796 0.761 0.782 0.810 0.795 0.77 0.81 0.788
J48 0.681 0.761 0.719 *0.796 *0.847 *0.811 0.779 0.847 0.801
Random Forest 0.689 0.814 0.747 0.740 *0.847 0.790 0.74 0.847 0.790
Random Tree 0.736 0.708 0.721 0.773 0.788 0.781 0.776 0.766 0.771
PCA Bayes Net 0.681 0.761 0.719 0.770 0.810 0.788 0.806 *0.854 0.816
Naïve Bayes 0.749 0.743 0.746 0.770 0.810 0.788 0.806 *0.854 0.816
Logistic 0.688 0.805 0.742 0.740 0.847 0.790 0.740 0.847 0.790
MLP 0.700 0.752 0.724 0.742 0.759 0.750 0.782 0.832 0.802
J48 0.691 0.823 0.751 0.738 0.832 0.782 0.740 0.847 0.790
Random Forest 0.688 0.805 0.742 0.740 *0.847 0.790 0.740 0.847 0.790
Random Tree 0.741 0.752 0.747 0.766 0.766 0.766 *0.825 0.839 *0.831