# Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art
## 3 Results and Discussion  
### Sentiment Analysis Model Performance Evaluation  
Firstly, the text sentiment analysis model proposed in this paper and the baseline models are evaluated from a quantitative analysis standpoint, using the evaluation metrics described in Section 2.3. All the models were trained for 50 epochs, and the best performances were recorded for a benchmark comparison. The results are shown in Tables 6 and 7. \begin{table}
\begin{tabular}{c c c} \hline  & **Actual Positive** & **Actual Negative** \\ \hline
**Predicted Positive** & TP & FP \\
**Predicted Negative** & FN & TN \\ \hline \end{tabular}
\end{table} 
Table 5: The definition of the confusion matrix. As the results indicate, the Word2 vec_BiLSTM model exhibits worse performance across the evaluation metrics. As a static text representation model, Word2 vec is constrained by its shallow network architecture and the limited contextual window, which hinders its ability to effectively address challenges like polysemy and non-standard expressions in Weibo posts, thereby impacting the efficiency of extracting profound semantic features from Weibo posts. Benefiting from the MLM and bidirectional Transformer architecture, the BERT-WWM model demonstrated better efficacy in text representation than Word2 vec. Moreover, the LERT pre-trained model, augmented with linguistic features and multi-task learning, achieved better performance than the BERT-WWM. The integration of linguistic features enhances the model's capacity to comprehend and depict intricate semantics, thereby improving the performance of sentiment analysis when handling complex corpora. In comparison with the model utilizing the text representation pooled from the model for classification, the integration of LSTM, SRU, and SRU++ demonstrated additional enhancements across the evaluation metrics. When applied to the Weibo corpus, pre-trained models exhibited limited effectiveness in capturing distinctive expressions and features unique to the Weibo platform. The incorporation of RNNs enhances the model's ability to emphasize sequential characteristics in Weibo posts. By capturing long-distance dependencies, the model can effectively acquire the emotional semantic features within the text, consequently enhancing the classification accuracy of the model. To investigate the influence of the self-attention module embedded in the SRU++ unit, this paper visualizes the variation process of the \(F_{1}\) value and loss value during the training process of LERT_BiSRU and LERT_BiSRU++ on both the weibo2018 dataset and the AI Art Weibo topic dataset, as illustrated in Figures 9 and 10. As illustrated in Figures 9 and 10, the LERT_BiSRU++ model demonstrated a quicker convergence rate compared to the LERT_BiSRU model during the learning phase, displaying a more consistent and smoother curve in both \(F_{1}\) value and loss value. By incorporating a self-attention module in the batch multiplication process, SRU++ benefits from the feature selection process provided by the self-attention calculation process, enabling the model to identify crucial features in the word embedding provided by the LERT model. Consequently, the LERT_BiSRU++ model achieves a faster learning process and higher classification accuracy. \begin{table}
\begin{tabular}{l c c c c} \hline  & **Acc** & **P** & **R** & **F1** \\ \hline Word2 vec\_BiLSTM & 0.856 & 0.862 & 0.861 & 0.862 \\ BERT-WWM & 0.903 & 0.881 & 0.885 & 0.883 \\ LERT & 0.907 & 0.884 & 0.887 & 0.886 \\ LERT\_BiLSTM & 0.922 & 0.907 & 0.911 & 0.909 \\ LERT\_BiSRU & 0.918 & 0.905 & 0.903 & 0.904 \\ LERT\_BiSRU++ & **0.932** & **0.921** & **0.918** & **0.92** \\ \hline \end{tabular}
\end{table} 
Table 6: The experiment results on the weibo2018 dataset.