Table 13.

Comparison between sentiment classification models.

Classification models	IMDB		Yelp 2013		Yelp 2014
	Accuracy	RMSE	Accuracy	RMSE	Accuracy	RMSE
Without using user and product information
Majority	0.196	2.495	0.411	1.060	0.392	1.097
Trigram	0.399	1.783	0.569	0.814	0.577	0.804
Text feature	0.402	1.793	0.556	0.845	0.572	0.800
AvgWordvec + SVM	0.304	1.985	0.526	0.898	0.530	0.893
SSWE + SVM	0.312	1.973	0.549	0.849	0.557	0.851
Paragraph vector	0.341	1.814	0.554	0.832	0.564	0.802
RNTN + recurrent	0.400	1.764	0.574	0.804	0.582	0.821
CNN and without UP (UPNN)	0.405	1.629	0.577	0.812	0.585	0.808
NSC	0.443	1.465	0.627	0.701	0.637	0.686
NSC + LA	0.487	1.381	0.631	0.706	0.630	0.715
Using user and product information
Trigram + UPF	0.404	1.764	0.570	0.803	0.576	0.789
Text feature + UPF	0.402	1.774	0.561	1.822	0.579	0.791
JMARS	N/A	1.773	N/A	0.985	N/A	0.999
UPNN (CNN)	0.435	1.602	0.596	0.784	0.608	0.764
UPNN (NSC)	0.471	1.443	0.631	0.702	N/A	N/A
NSC + UMA	0.533	1.281	0.650	0.692	0.667	0.654