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Table 10 Experiment results of wine reviews

From: Toward multi-label sentiment analysis: a transfer learning based approach

Model

Accuracy (%)

Hamming loss

Macro F1

Micro F1

Proposed models

 BERT

79.13

0.021

0.86

0.92

 XLNet

78.41

0.021

0.86

0.92

Baseline deep learning models

 LSTM

58.01

0.037

0.72

0.85

 BiLSTM

56.75

0.039

0.73

0.85

 CNN + LSTM

51.92

0.042

0.64

0.83

Baseline machine learning models

 SGD + OR

34.10

0.053

0.58

0.77

 LR + OR

38.03

0.049

0.65

0.80

 SVC + OR

47.92

0.041

0.77

0.84

 RF + OR

64.97

0.029

0.83

0.88

 SGD + BR

33.81

0.053

0.58

0.77

 LR + BR

38.03

0.049

0.64

0.80

 SVC + BR

47.92

0.041

0.77

0.84

 RF + BR

63.35

0.030

0.82

0.88

 SGD + CC

50.51

0.056

0.62

0.78

 LR + CC

54.24

0.052

0.67

0.80

 SVC + CC

64.12

0.039

0.79

0.85

 RF + CC

68.06

0.030

0.83

0.88

 SGD + LP

58.25

0.051

0.72

0.80

 LR + LP

58.16

0.049

0.84

0.87

 SVC + LP

70.94

0.034

0.84

0.87

 RF + LP

72.54

0.035

0.82

0.87

 SGD + RakelD

46.95

0.052

0.70

0.80

 LR + RakelD

56.13

0.049

0.68

0.81

 SVC + RakelD

57.47

0.038

0.83

0.85

 RF + RakelD

70.82

0.034

0.83

0.87

 BRkNNa

45.91

0.062

0.67

0.76

 BRkNNb

46.13

0.060

0.66

0.77

 MLARAM

50.53

0.044

0.71

0.79

 MLkNN

48.25

0.055

0.68

0.77