# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.2 Micro Comparison Analysis of Model and Baseline Model  
In this subsection, we provide a comprehensive comparison of the Bi-Dual Inference model with six other baseline models (SVM, LSTM, Bi-LSTM, CNN-BiLSTM, C-GRU, MF-CSEL) on two distinct datasets for six-tuple emotion classification (happy, sad, angry, surprised, fear, disgust). The results of this comparison are presented in Tables 4 and 5. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Emotion**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Evaluation Metrics**} & \multirow{2}{*}{**Emotion**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Evaluation Metrics**} \\ \cline{3-3} \cline{5-10}  & & & **P\%** & & **R\%** & & **F1\%** & **P\%** & **R\%** & **F1\%** \\ \hline \multirow{8}{*}{Happy} & SVM & 55.22 & 65.31 & 59.84 & \multirow{8}{*}{Disgusted} & SVM & 50.15 & 52.31 & 51.20 \\  & LSTM & 57.44 & 26.76 & 36.51 & & LSTM & 52.46 & 43.36 & 47.54 \\  & Bi-LSTM & 67.02 & 60.30 & 63.48 & & Bi-LSTM & 55.77 & 48.22 & 51.72 \\  & CNN-BiLSTM & 80.12 & 65.55 & 72.10 & \multirow{2}{*}{Disgusted} & CNN-BiLSTM & 60.22 & 65.88 & 62.92 \\  & C-GRU & 55.71 & **80.03** & 65.69 & & C-GRU & 62.15 & 55.77 & 58.78 \\  & MF-CSEL & 68.00 & 69.52 & 68.70 & & MF-CSEL & 65.55 & 70.20 & 67.79 \\  & Bi-Dual Inference & **85.53** & 68.88 & **76.30** & & Bi-Dual Inference & **70.02** & **73.55** & **71.74** \\ \hline \multirow{8}{*}{Surprised} & SVM & 16.22 & 36.24 & 22.40 & \multirow{8}{*}{Fear} & SVM & 20.33 & **66.77** & 31.16 \\  & LSTM & 52.41 & 47.12 & 49.63 & & LSTM & 27.12 & 36.77 & 31.24 \\  & Bi-LSTM & 45.51 & 35.62 & 39.94 & & Bi-LSTM & 54.89 & 50.77 & 52.99 \\  & CNN-BiLSTM & 55.77 & 53.27 & 54.49 & & CNN-BiLSTM & **60.11** & 56.22 & **58.09** \\  & C-GRU & 50.22 & 38.77 & 43.75 & & C-GRU & 45.21 & 40.66 & 42.81 \\  & MF-CSEL & 37.21 & 32.66 & 34.70 & & MF-CSEL & 16.50 & 32.72 & 21.93 \\  & Bi-Dual Inference & **60.23** & **65.55** & **62.77** & & Bi-Dual Inference & 50.11 & 62.22 & 55.51 \\ \hline \multirow{8}{*}{Angry} & SVM & 25.32 & 55.70 & 34.81 & \multirow{8}{*}{Sad} & SVM & 26.95 & 60.34 & 37.25 \\  & LSTM & 51.43 & 51.97 & 33.97 & & LSTM & 57.51 & 56.58 & 56.88 \\ \cline{1-1}  & Bi-LSTM & 50.12 & 40.52 & 44.79 & & Bi-LSTM & 52.20 & 50.31 & 51.23 \\ \cline{1-1}  & CNN-BiLSTM & **66.73** & 68.92 & **67.80** & & CNN-BiLSTM & 60.80 & 54.48 & 57.46 \\ \cline{1-1}  & C-GRU & 55.35 & 60.02 & 57.59 & & C-GRU & 59.99 & 70.22 & 64.70 \\ \cline{1-1}  & MF-CSEL & 47.71 & 33.15 & 39.05 & & MF-CSEL & 69.02 & 33.41 & 45.03 \\ \cline{1-1}  & Bi-Dual Inference & 65.20 & **70.22** & 67.61 & & Bi-Dual Inference & **70.25** & **75.58** & **72.81** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sentiment classification performance of different models on NLPCC2013 dataset. Figure 6: Overall performance of different models on NLPCC2013 and NLPCC2014. Based on the information provided in the two tables, it is evident that our proposed Bi-Dual Inference model achieves the highest precision (\(P\)), recall (\(R\)), and \(F1\) scores in multiple emotion label categories. This clearly demonstrates the effectiveness of our model in comparison to the other models. Specifically, when comparing the NLPCC 2013 and NLPCC 2014 datasets, all models show improvement to some extent due to the increase in training data size. However, our proposed model exhibits the most significant improvement as a result of the larger training data size. This is attributed to the increased feedback signals between the dual model and primal model, leading to enhanced performance.This improvement is particularly evident in the 'fear' emotion label, where the \(F1\) score reaches its highest performance in our model, increasing from 55.51% to 61.82%. Additionally, there is a notable difference in performance for certain emotion labels, specifically 'happy' and 'fear'. This discrepancy may be attributed to the fact that people tend to share more content that brings them joy on social networks, while fearful emotions are often not shared as frequently.