# 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
Consequently, this results in a larger volume of certain emotions being present in the social network data. To provide a visual representation of the performance comparison among the models, we extracted the precision (\(P\)), recall (\(R\)), and \(F1\) scores for each of the six emotions presented in Tables 4 and 5. These scores are depicted in Figures 7 and 8. By analyzing these two figures, it becomes evident that our proposed model outperforms the others in terms of precision (\(P\)), recall (\(R\)), and \(F1\) scores for most sentiment labels. The scores for our model exhibit a more consistent trend without significant fluctuations. On the other hand, when examining the other models, the SVM model exhibits noticeably lower accuracy in terms of precision (\(P\)) and \(F1\) scores specifically for the fear emotion labels. This suggests that the overall performance of the other models is comparatively lower in the analysis of the six emotion categories, with a higher proportion of correctly identified emotion labels out of the total number of correct labels identified. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Emotion**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Evaluation Metrics**} & \multirow{2}{*}{**Emotion**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Evaluation Metrics**} \\ \cline{3-3} \cline{6-10}  & & **P\%** & & & & **R\%** & **F1\%** \\ \hline \multirow{6}{*}{Happy} & SVM & 61.00 & 69.10 & 64.80 & & SVM & 56.39 & 62.15 & 60.01 \\  & LSTM & 54.88 & 58.36 & 56.49 & & LSTM & 56.57 & 72.44 & 63.53 \\  & Bi-LSTM & 68.22 & 62.15 & 65.04 & & Bi-LSTM & 52.31 & 55.33 & 56.86 \\  & CNN-BiLSTM & 82.21 & 70.22 & 75.74 & Disgusted & CNN-BiLSTM & 64.55 & 70.02 & 67.17 \\  & C-GRU & 56.22 & 78.05 & 65.36 & & C-GRU & 58.33 & 50.75 & 54.27 \\  & MF-CSEL & 65.90 & **79.60** & 72.61 & & MF-CSEL & 66.77 & 70.08 & 68.38 \\  & Bi-Dual Inference & **86.68** & 75.22 & **80.54** & & Bi-Dual Inference & **72.25** & **75.38** & **73.78** \\ \hline \multirow{6}{*}{Surprised} & SVM & 14.20 & 39.12 & 20.83 & & SVM & 25.66 & **78.81** & 38.62 \\  & LSTM & 52.63 & 54.24 & 53.22 & & LSTM & 53.50 & 52.95 & 53.22 \\  & Bi-LSTM & 48.88 & 40.22 & 44.12 & & Bi-LSTM & 59.50 & 51.88 & 55.42 \\  & CNN-BiLSTM & 60.58 & 65.99 & 63.16 & Fear & CNN-BiLSTM & **62.34** & 54.37 & 58.08 \\  & C-GRU & 49.33 & 45.77 & 47.48 & & C-GRU & 50.22 & 45.10 & 47.52 \\  & MF-CSEL & 49.21 & 32.66 & 39.22 & & MF-CSEL & 17.25 & 32.74 & 22.55 \\  & Bi-Dual Inference & **63.55** & **68.22** & **65.80** & & Bi-Dual Inference & 55.22 & 70.22 & **61.82** \\ \hline \multirow{6}{*}{Angry} & SVM & 29.92 & 66.70 & 41.31 & & SVM & 30.71 & 65.22 & 41.75 \\  & LSTM & 51.05 & 47.13 & 50.65 & & LSTM & 53.44 & 42.92 & 47.61 \\ \cline{1-1}  & Bi-LSTM & 55.21 & 50.31 & 52.64 & & Bi-LSTM & 53.10 & 49.33 & 51.14 \\ \cline{1-1}  & CNN-BiLSTM & 70.22 & 72.11 & 71.15 & Sad & CNN-BiLSTM & 65.66 & 60.77 & 63.12 \\ \cline{1-1}  & C-GRU & 60.09 & 58.22 & 59.14 & & C-GRU & 65.11 & 69.27 & 67.12 \\ \cline{1-1}  & MF-CSEL & 54.40 & 45.21 & 49.33 & & MF-CSEL & 49.00 & 53.30 & 51.22 \\ \cline{1-1}  & Bi-Dual Inference & **70.31** & **74.22** & **72.21** & & Bi-Dual Inference & **71.05** & **76.63** & **73.73** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sentiment classification performance of different models on NLPCC2014 dataset.