Table 21.

Sentiment classification using different sentiment dictionaries achieved by WJST1.

Model\dataset	Android	Auto	ELEC	MOV	STS	Sanders	SOMD
RND	0.48	0.4925	0.465	0.525	0.5346	0.4852	0.4847
AFINN	0.6975	0.625	0.675	0.595	0.734	0.674	0.3395
AFINN + w	0.685	0.58	0.6375	0.595	—	—	—
AFINN + RND	0.58	0.535	0.52	0.555	0.6038	0.5424	0.4475
Bing_Liu	0.6975	0.64	0.695	0.635	0.698	0.6813	0.322
Bing_Liu + RND	0.5775	0.535	0.535	0.565	0.6288	0.5416	0.4388
IMDB	0.7025	0.59	0.6375	0.6425	0.5512	0.5988	0.4617
IMDB + RND	0.6125	0.4825	0.5475	0.5975	0.5595	0.5196	0.4748
8K	0.5425	0.5025	0.5075	0.5025	0.4986	0.5351	0.3995
8K + RND	0.515	0.48	0.5075	0.51	0.5373	0.495	0.4814
Android	0.855	0.6025	0.565	0.65	0.5857	0.5865	0.4985
Android + w	0.835	0.6	0.6025	0.6625	—	—	—
Android + RND	0.855	0.59	0.575	0.6125	0.5967	0.5579	0.505
Auto	0.6375	0.745	0.6175	0.63	0.594	0.6233	0.4766
Auto + w	0.6325	0.7375	0.65	0.635	—	—	—
Auto + RND	0.6375	0.745	0.62	0.6225	0.5829	0.6118	0.4875
ELEC	0.65	0.5475	0.7075	0.5825	0.594	0.6192	0.457
ELEC + w	0.63	0.5775	0.7125	0.63	—	—	—
ELEC + RND	0.6425	0.5575	0.7075	0.5775	0.6134	0.5914	0.4744
MOV	0.63	0.5525	0.5825	0.6375	0.5663	0.5767	0.4603
MOV + w	0.6425	0.5475	0.6425	0.94	—	—	—
MOV + RND	0.635	0.565	0.61	0.6375	0.5718	0.571	0.4493
STS	0.585	0.5475	0.595	0.5775	0.7431	0.5939	0.4231
STS + RND	0.5875	0.565	0.5975	0.5725	0.7431	0.5743	0.4395
Sanders	0.605	0.555	0.6	0.5375	0.6632	0.7538	0.421
Sanders + RND	0.6075	0.55	0.6025	0.5375	0.6771	0.7538	0.4559
SOMD	0.615	0.555	0.55	0.5125	0.5773	0.6078	0.594
SOMD + RND	0.61	0.5275	0.5425	0.5325	0.594	0.5767	0.594