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. 2021 May 6;226:107126. doi: 10.1016/j.knosys.2021.107126

Table 2.

The results of sentiment classification for individual datasets.

Dataset
 Classifier
 Accuracy
 AUC
 F-Measure
 Sensitivity
 Specificity
 Dataset
 Accuracy
 AUC
 F-Measure
 Sensitivity
 Specificity
Traditional Analysis TClustVID
Dataset-01 LSTM 0.927 0.897 0.926 0.927 0.868 Cluster-01 0.983 0.979 0.983 0.983 0.974
DT 0.915 0.901 0.916 0.915 0.886 0.952 0.945 0.952 0.952 0.938
GB 0.788 0.653 0.746 0.788 0.518 0.816 0.713 0.790 0.816 0.610
KNN 0.910 0.880 0.909 0.910 0.850 0.946 0.930 0.945 0.946 0.914
LR 0.695 0.502 0.576 0.695 0.308 0.679 0.500 0.552 0.679 0.322
MLP 0.840 0.766 0.830 0.840 0.692 0.901 0.869 0.899 0.901 0.836
NB 0.654 0.503 0.597 0.654 0.352 0.644 0.502 0.577 0.644 0.359
RF 0.924 0.898 0.923 0.924 0.873 0.957 0.944 0.957 0.957 0.932
SVM 0.757 0.600 0.694 0.757 0.442 0.803 0.693 0.772 0.803 0.583
XGB 0.787 0.657 0.750 0.787 0.527 0.854 0.774 0.840 0.854 0.695
Dataset-02 LSTM 0.968 0.949 0.968 0.968 0.929 Cluster-02 0.988 0.977 0.988 0.988 0.967
DT 0.931 0.899 0.931 0.931 0.866 0.964 0.943 0.964 0.964 0.921
GB 0.816 0.567 0.755 0.816 0.318 0.856 0.626 0.819 0.856 0.396
KNN 0.924 0.865 0.922 0.924 0.806 0.958 0.918 0.957 0.958 0.878
LR 0.787 0.501 0.695 0.787 0.216 0.807 0.505 0.727 0.807 0.203
MLP 0.867 0.730 0.854 0.867 0.592 0.925 0.841 0.921 0.925 0.756
NB 0.213 0.500 0.076 0.213 0.787 0.192 0.500 0.063 0.192 0.808
RF 0.937 0.888 0.936 0.937 0.838 0.968 0.936 0.967 0.968 0.905
SVM 0.787 0.501 0.695 0.787 0.215 0.806 0.502 0.724 0.806 0.197
XGB 0.820 0.578 0.764 0.820 0.336 0.875 0.694 0.855 0.875 0.513
Dataset-03 LSTM 0.915 0.922 0.915 0.915 0.929 Cluster-03 0.985 0.987 0.985 0.985 0.988
DT 0.911 0.930 0.911 0.911 0.950 0.960 0.967 0.960 0.960 0.973
GB 0.699 0.717 0.674 0.699 0.735 0.846 0.838 0.836 0.846 0.830
KNN 0.893 0.918 0.893 0.893 0.942 0.950 0.958 0.950 0.950 0.965
LR 0.514 0.548 0.426 0.514 0.582 0.668 0.651 0.628 0.668 0.635
MLP 0.793 0.827 0.788 0.793 0.860 0.909 0.914 0.908 0.909 0.918
NB 0.485 0.551 0.441 0.485 0.616 0.212 0.504 0.178 0.212 0.796
RF 0.911 0.933 0.911 0.911 0.955 0.959 0.966 0.959 0.959 0.973
SVM 0.344 0.520 0.308 0.344 0.696 0.463 0.617 0.482 0.463 0.770
XGB 0.722 0.766 0.710 0.722 0.809 0.847 0.846 0.838 0.847 0.845
Dataset-04 LSTM 0.904 0.915 0.903 0.904 0.926 Cluster-04 0.957 0.957 0.956 0.957 0.956
DT 0.892 0.915 0.892 0.892 0.937 0.943 0.949 0.943 0.943 0.956
GB 0.621 0.614 0.553 0.621 0.607 0.818 0.788 0.806 0.818 0.758
KNN 0.873 0.901 0.873 0.873 0.929 0.930 0.939 0.930 0.930 0.948
LR 0.547 0.556 0.457 0.547 0.565 0.747 0.722 0.728 0.747 0.697
MLP 0.765 0.797 0.758 0.765 0.829 0.882 0.877 0.878 0.882 0.872
NB 0.533 0.536 0.422 0.533 0.539 0.274 0.506 0.139 0.274 0.737
RF 0.892 0.918 0.892 0.892 0.943 0.943 0.950 0.942 0.943 0.958
SVM 0.397 0.519 0.398 0.397 0.641 0.326 0.523 0.340 0.326 0.720
XGB 0.683 0.691 0.648 0.683 0.699 0.825 0.809 0.817 0.825 0.792
Dataset-05 LSTM 0.904 0.927 0.903 0.904 0.951 Cluster-05 0.968 0.975 0.968 0.968 0.983
DT 0.866 0.899 0.866 0.866 0.932 0.902 0.925 0.902 0.902 0.949
GB 0.534 0.625 0.494 0.534 0.715 0.624 0.684 0.587 0.624 0.744
KNN 0.841 0.880 0.841 0.841 0.920 0.878 0.907 0.878 0.878 0.937
LR 0.431 0.552 0.367 0.431 0.673 0.454 0.557 0.386 0.454 0.659
MLP 0.624 0.712 0.622 0.624 0.801 0.749 0.800 0.744 0.749 0.851
NB 0.419 0.529 0.305 0.419 0.639 0.429 0.524 0.344 0.429 0.619
RF 0.865 0.900 0.865 0.865 0.934 0.900 0.924 0.900 0.900 0.949
SVM 0.338 0.525 0.258 0.338 0.711 0.424 0.537 0.362 0.424 0.650
XGB 0.548 0.647 0.532 0.548 0.745 0.645 0.717 0.639 0.645 0.789
Dataset-06 LSTM 0.876 0.908 0.877 0.876 0.941 Cluster-06 0.977 0.982 0.977 0.977 0.987
DT 0.879 0.909 0.879 0.879 0.938 0.932 0.948 0.932 0.932 0.963
GB 0.602 0.659 0.562 0.602 0.715 0.763 0.785 0.748 0.763 0.807
KNN 0.858 0.893 0.859 0.858 0.929 0.917 0.936 0.917 0.917 0.955
LR 0.474 0.561 0.400 0.474 0.648 0.526 0.581 0.465 0.526 0.636
MLP 0.714 0.778 0.712 0.714 0.842 0.846 0.874 0.845 0.846 0.902
NB 0.450 0.522 0.315 0.450 0.594 0.475 0.515 0.328 0.475 0.554
RF 0.879 0.910 0.879 0.879 0.942 0.931 0.948 0.931 0.931 0.964
SVM 0.418 0.530 0.341 0.418 0.643 0.536 0.568 0.433 0.536 0.600
XGB 0.642 0.719 0.637 0.642 0.796 0.774 0.813 0.772 0.774 0.851
Dataset-07 LSTM 0.903 0.919 0.903 0.903 0.936 Cluster-07 0.983 0.986 0.983 0.983 0.990
DT 0.908 0.929 0.908 0.908 0.951 0.955 0.965 0.955 0.955 0.975
GB 0.664 0.718 0.656 0.664 0.773 0.810 0.830 0.806 0.810 0.850
KNN 0.889 0.915 0.889 0.889 0.942 0.941 0.954 0.941 0.941 0.967
LR 0.451 0.538 0.380 0.451 0.624 0.548 0.598 0.501 0.548 0.647
MLP 0.768 0.813 0.764 0.768 0.859 0.885 0.905 0.885 0.885 0.925
NB 0.219 0.501 0.083 0.219 0.783 0.220 0.503 0.094 0.220 0.787
RF 0.909 0.931 0.909 0.909 0.954 0.954 0.964 0.954 0.954 0.975
SVM 0.353 0.517 0.353 0.353 0.681 0.299 0.539 0.251 0.299 0.780
XGB 0.635 0.705 0.632 0.635 0.774 0.815 0.843 0.814 0.815 0.871
Dataset-08 LSTM 0.908 0.921 0.907 0.908 0.935 Cluster-08 0.976 0.981 0.976 0.976 0.985
DT 0.870 0.901 0.870 0.870 0.931 0.910 0.929 0.910 0.910 0.948
GB 0.600 0.654 0.557 0.600 0.709 0.687 0.698 0.655 0.687 0.708
KNN 0.847 0.884 0.847 0.847 0.921 0.853 0.884 0.853 0.853 0.915
LR 0.501 0.582 0.440 0.501 0.663 0.516 0.547 0.428 0.516 0.578
MLP 0.650 0.722 0.635 0.650 0.794 0.795 0.825 0.790 0.795 0.856
NB 0.460 0.529 0.332 0.460 0.599 0.489 0.536 0.379 0.489 0.582
RF 0.870 0.903 0.870 0.870 0.936 0.909 0.930 0.909 0.909 0.951
SVM 0.440 0.513 0.326 0.440 0.585 0.409 0.505 0.337 0.409 0.601
XGB 0.597 0.678 0.577 0.597 0.759 0.678 0.724 0.669 0.678 0.770
Dataset-09 LSTM 0.897 0.912 0.896 0.897 0.928 Cluster-09 0.976 0.981 0.976 0.976 0.986
DT 0.870 0.900 0.870 0.870 0.931 0.911 0.930 0.911 0.911 0.949
GB 0.600 0.654 0.557 0.600 0.709 0.686 0.698 0.651 0.686 0.711
KNN 0.847 0.884 0.847 0.847 0.921 0.856 0.886 0.856 0.856 0.917
LR 0.498 0.579 0.437 0.498 0.660 0.508 0.541 0.420 0.508 0.574
MLP 0.650 0.715 0.633 0.650 0.780 0.802 0.830 0.797 0.802 0.859
NB 0.221 0.500 0.083 0.221 0.780 0.250 0.507 0.191 0.250 0.764
RF 0.869 0.902 0.870 0.869 0.936 0.910 0.931 0.910 0.910 0.952
SVM 0.345 0.508 0.300 0.345 0.671 0.270 0.515 0.243 0.270 0.759
XGB 0.599 0.680 0.579 0.599 0.760 0.676 0.726 0.668 0.676 0.776