# A Multi-Stance Detection Method by Fusing Sentiment Features
## 4 Experiment
### Results Analysis
At the same time, combining stance detection and sentiment analysis can reduce the interference of moody expressions. In this paper, the sentiment lexicon is used to calculate the sentiment score of the review, and a serial strategy is used to fuse the sentiment features with the textual features. Table 7 shows that by fusing the sentiment features, BCF outperforms bi-LSTM-CNN on \(F_{w}\) by 2.58%. Furthermore, compared to BAC, BACF improves \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{P_{w}}\) & \(\mathbf{R_{w}}\) & \(\mathbf{F_{w}}\) \\ \hline bi-LSTM-CNN & 80.32\% & 80.37\% & 79.33\% \\ BCF & 82.17\% & 81.95\% & 81.91\% \\ BAC & 82.18\% & 82.20\% & 82.07\% \\ BACF & 84.42\% & 83.99\% & 84.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results of ablation study. Figure 3: Length statistics of reviews. \(P_{w}\), \(R_{w}\), and \(F_{w}\) by 3.24%, 1.79%, and 1.96%, respectively. The experiment demonstrates that this model can efficiently employ sentiment information to assist the model in achieving stance detection, which improves the model's prediction performance. Therefore, the fusion of sentiment features can help the model perform stance detection more effectively. The attention mechanism can improve the model's predictions. By utilizing an attention mechanism, the model can concentrate on crucial information and improve prediction accuracy by giving different weights. BAC improves the model's performance by giving more weight to significant information and less weight to irrelevant information via the attention mechanism. Table 7 shows that BAC performs better than bi-LSTM-CNN on \(P_{w}\), \(R_{w}\), and \(F_{w}\) through improvements of 1.86%, 1.83%, and 2.74%, respectively. The results show that the performance of the model can be significantly improved by introducing an attention mechanism. Therefore, by weighting the important information, the attention mechanism allows the model to predict the stance more accurately. Verification of generalization  
To verify the generalization of BACF, this research selects open-source datasets for experiments. Since the current research does not have the same task, this paper chooses a dataset in the field of opinion mining. This dataset is about hotel reviews and includes 2444 negative reviews and 5322 positive reviews. One of the negative reviews is empty, resulting in a total of 7765 reviews. The dataset was divided into training, validation, and test sets in the ratio of 6:2:2. The experimental results are shown in Table 8.  
Table 8 shows that the BACF proposed in this study has the best performance, with a \(P_{w}\), \(R_{w}\), and \(F_{w}\) of 85.80%, 85.07%, and 85.29%, respectively. It is followed by bi-LSTM-Att, which incorporates the attention mechanism and has an \(F_{w}\) of 84.69%. TextRCNN using a pooling layer also achieves good results with an \(F_{w}\) of 83.59%. The lowest results come from LSTM, which has an \(F_{w}\) of only 71.21%, which should be due to the fact that LSTM is not effective at processing short texts. As a result, the BACF proposed in this paper is more generalizable. In conclusion, the BACF proposed in this paper employs a hybrid neural network structure, introduces sentiment information via feature fusion, uses the attention mechanism to weight essential information, and outperforms other approaches in the stance detection task. In comparison to traditional deep learning models, BACF can effectively carry out stance detection and has a better accuracy rate.