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 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
		BF			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

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