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Table 11 Experiment results of movie reviews

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

Model

Accuracy (%)

Hamming loss

Macro F1

Micro F1

Proposed models

 BERT

87.57

0.011

0.95

0.96

 XLNet

89.86

0.009

0.94

0.97

Baseline deep learning models

 LSTM

76.99

0.021

0.87

0.92

 BiLSTM

71.34

0.025

0.82

0.90

 CNN + LSTM

76.73

0.021

0.87

0.92

Baseline machine learning models

 SGD + OR

74.88

0.022

0.94

0.92

 LR + OR

76.11

0.021

0.92

0.92

 SVC + OR

80.78

0.017

0.98

0.94

 RF + OR

73.40

0.023

0.95

0.92

 SGD + BR

75.06

0.022

0.94

0.92

 LR + BR

76.11

0.021

0.92

0.92

 SVC + BR

80.78

0.017

0.98

0.94

 RF + BR

72.07

0.023

0.95

0.92

 SGD + CC

75.16

0.022

0.95

0.92

 LR + CC

76.25

0.021

0.92

0.92

 SVC + CC

80.88

0.017

0.98

0.94

 RF + CC

72.99

0.024

0.94

0.91

 SGD + LP

74.58

0.023

0.94

0.92

 LR + LP

74.36

0.024

0.95

0.91

 SVC + LP

76.08

0.021

0.98

0.92

 RF + LP

72.64

0.024

0.97

0.91

 SGD + RakelD

72.73

0.024

0.94

0.91

 LR + RakelD

73.58

0.025

0.93

0.91

 SVC + RakelD

76.42

0.021

0.98

0.93

 RF + RakelD

72.56

0.024

0.97

0.91

 BRkNNa

71.93

0.025

0.96

0.91

BRkNNb

56.63

0.042

0.89

0.81

 MLARAM

30.85

0.048

0.89

0.82

 MLkNN

61.26

0.029

0.91

0.88