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Sentiment analysis and validity evaluation of Japanese language under the transfer learning model

Xiaodan Li

Xiaodan Li

School of Foreign Languages, Liaodong UniversityDandong, China
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Li, Xiaodan
  
21 mar 2025
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Pubblicato online: 21 mar 2025
Ricevuto: 31 ott 2024
Accettato: 19 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0606
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© 2025 Xiaodan Li, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Multi-step transfer learning flowchart
Multi-step transfer learning flowchart

Figure 2.

Visualization of experimental results
Visualization of experimental results

Figure 3.

The effects of different α values on cross-language emotional classification
The effects of different α values on cross-language emotional classification

Figure 4.

Example (1) of word vector representation
Example (1) of word vector representation

Figure 5.

Example (2) of word vector representation
Example (2) of word vector representation

The experimental results of ours and the baseline method(non-transfer)

Model R L T
Acc (%) Macro-F1 (%) Acc (%) Macro-F1 (%) Acc (%) Macro-F1 (%)
TD-LSTM 75.63 64.25 68.12 62.28 66.58 63.98
AE-LSTM 69.05 62.54 69.03 62.46 69.32 56.39
ATAE-LSTM 77.26 65.10 68.74 62.33 69.69 56.90
MemNet 78.05 65.85 70.36 63.97 68.44 67.11
IAN 78.57 -- 72.23 -- -- --
RAM 78.41 68.56 72.00 68.51 69.52 67.24
SACA 82.10 73.15 76.31 73.04 72.76 71.07
BERT 82.93 74.08 76.99 73.69 75.04 73.57
RoBERTa 83.71 74.50 72.43 69.55 70.83 68.76

The influence of the word vector dimension on emotional analysis

Method 50 Dimension 100 Dimension 150 Dimension 200 Dimension
Accuracy F1 Accuracy F1 Accuracy F1 Accuracy F1
Upper 0.856 0.867 -- -- -- -- -- --
Machine translation 0.729 0.718 -- -- -- -- -- --
Bi_random 0.557 0.709 0.576 0.647 0.572 0.545 0.604 0.718
Bi_W2V 0.732 0.714 0.747 0.744 0.777 0.730 0.748 0.709
CLCDSA 0.602 0.632 0.689 0.693 0.720 0.715 0.786 0.799
Ours 0.818 0.839 0.776 0.771 0.800 0.733 0.778 0.700

The experimental results of ours and the baseline method(transferred)

Ds→Dt Indicator BERT MMD DAN MGAN Ours
R1→L Acc(%) 70.67 72.33 72.05 69.80 73.82
Macro-F1(%) 68.77 69.84 68.49 66.96 71.28
B→L Acc(%) 70.17 71.44 70.56 70.40 71.77
Macro-F1(%) 67.41 67.91 67.28 66.82 68.11
H→L Acc(%) 70.74 71.35 71.43 70.79 72.26
Macro-F1(%) 67.05 67.98 67.79 67.84 69.15
B→R2 Acc(%) 76.66 78.89 78.34 72.71 79.31
Macro-F1(%) 69.03 69.78 70.86 64.26 70.93
H→R2 Acc(%) 75.49 79.57 78.87 72.21 79.97
Macro-F1(%) 68.24 70.74 70.03 62.52 71.91
R1→T Acc(%) 60.60 69.24 68.32 46.50 70.16
Macro-F1(%) 58.33 67.23 66.01 45.77 68.38
B→T Acc(%) 61.30 70.79 70.11 46.36 71.41
Macro-F1(%) 59.94 68.21 68.23 45.76 68.74
H→T Acc(%) 60.91 69.62 68.96 47.52 71.06
Macro-F1(%) 58.21 67.33 67.24 46.77 69.58
Lingua:
Inglese
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1 volte all'anno
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