Table 9 Error analysis of IERT-HDLMWEP technique with existing methods.

From: Empowering people with intellectual disabilities using integrated deep learning architecture driven enhanced text-based emotion classification

Approach

\(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\)

\(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\)

\(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\)

\(\:{\varvec{F}}_{\varvec{M}\varvec{e}\varvec{a}\varvec{s}\varvec{u}\varvec{r}\varvec{e}}\)

RoBERTa

10.25

13.19

13.27

17.07

BiGRU

5.21

6.12

17.66

13.72

RF

1.87

6.97

17.86

17.84

U-Net

11.03

13.80

13.95

17.63

Improved DBN-SVM

5.83

6.89

18.37

14.47

Bi-GRU

2.61

7.51

18.46

18.39

XLNet

3.43

7.82

19.25

18.78

DAN

9.94

8.63

17.37

14.45

Bi-LSTM

8.36

10.15

17.39

16.10

SVM

1.97

8.93

13.58

15.67

IERT-HDLMWEP

0.33

6.39

11.46

10.03

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