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

Rintyarna, Bagus Setya; Sarno, Riyanarto; Fatichah, Chastine

doi:10.1186/s40537-019-0246-8

Journal of Big Data

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
		BF			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

Back to article page