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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