# A Multi-Stance Detection Method by Fusing Sentiment Features
## 4 Experiment
### Results Analysis  
(1) Comparison experiments with traditional deep learning models  
The performance of BACF is contrasted with the traditional deep learning model to evaluate the model's efficiency. The experimental results are shown in Table 6.  
Table 6 shows that the BACF proposed in this paper achieves the best prediction performance, with the highest \(F_{w}\) of 84.03%. Among the traditional deep learning models, CNN outperforms LSTM, while GRU outperforms CNN. In terms of the dataset, reviews range in length from [1, 359], while Figure 3 depicts that almost 80% of texts have lengths of less than 61. LSTM processes sequence data through the forget gate, remember gate, and output gate, which is more suitable for long text. When the amount of data is not sufficient, it can easily lead to overfitting. CNN is excellent at processing local features, so the short texts are in its favor. However, when the LSTM extracts bidirectional information and combines an attention mechanism, the \(F_{w}\) of bi-LSTM-Att improves significantly and is 1.31% greater than that of CNN. GRU has a better prediction effect than CNN in \(P_{w}\), \(R_{w}\), and \(F_{w}\), respectively, because the structure of GRU is useful for processing sequence data. By combining the RNN structure with the maximum pooling layer, TextRCNN can appropriately capture textual information. Therefore, TextRCNN is the top performer among the traditional models. However, the \(F_{w}\) of this model is 79.20%, which is still approximately 4% lower than BACF, and all other models are below 80% in all metrics. BACF \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{P_{w}}\) & \(\mathbf{R_{w}}\) & \(\mathbf{F_{w}}\) \\ \hline LSTM & 73.82\% & 75.01\% & 73.86\% \\ CNN & 78.21\% & 78.33\% & 77.33\% \\ GRU & 78.71\% & 78.53\% & 78.57\% \\ bi-LSTM-Att & 79.45\% & 78.33\% & 78.64\% \\ TextRCNN & 79.82\% & 80.12\% & 79.20\% \\ bi-LSTM-CNN & 80.32\% & 80.37\% & 79.33\% \\ BACF & 84.42\% & 83.99\% & 84.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of comparison with traditional deep learning models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Learning Rate** & \(\mathbf{F_{w}}\) \\ \hline
0.0001 & 0.5629 \\
0.0003 & 0.7410 \\
0.001 & 0.7586 \\
0.003 & 0.7733 \\
0.01 & 0.8403 \\
0.03 & 0.3352 \\
0.1 & 0.2194 \\
0.3 & 0.2244 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Learning rate selection. is constructed by integrating deep learning, sentiment lexicon, and feature fusion. The experiment shows that, in comparison to the traditional deep learning model, the BACF proposed in this paper can more effectively achieve five-category stance detection. Hybrid neural networks can extract features more comprehensively, which can improve the accuracy of stance prediction. A review may contain important information scattered throughout. When posting reviews, users sometimes express their opinions right away, while other times they reserve them for the end or bury them in the midst. RNNs can be used to process sequence data. However, words that appear later typically have an advantage in RNNs. In contrast, CNNs are an impartial model with the advantage of extracting features by using kernels of various sizes. Hybrid neural networks that combine CNNs with bi-LSTM can employ CNNs to extract local features while benefiting from the advantages of RNNs that are good at processing sequential data. Table 6 shows that bi-LSTM-CNN performs better on \(P_{w}\), \(R_{w}\), and \(F_{w}\) when compared to the traditional single models, which shows that the hybrid neural network improves stance detection. (2) Ablation Study  
An ablation study was performed to demonstrate the importance of each module, and the results are shown in Table 7.  
Combining the sentiment lexicon with deep learning can help improve stance detection. Users usually express their attitudes while also expressing their sentiments. Taking sentimental factors into consideration might be helpful for identifying the user's stance.