# Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm
## 5 Micro-Blog Sentiment Classification Experiment
### Comparative Experiment  
The precision, recall, and F1 values are used as evaluation indicators in comparing the proposed PBAL approach with other benchmark techniques:  
1. SVM: The basic model of a Support Vector Machine (SVM) is to find the best-separated hyperplane in the feature space to maximize the positive and negative sample spacing on the training set. SVM is a supervised learning algorithm used to solve the two-class problem. It can also be used to solve nonlinear problems after the introduction of the kernel method. 2. LSTM: Proposed by [PERSON] and [PERSON] (1997), LSTM is a special type of RNN that helps in learning long-term dependency information. 3. CNN-rand: The CNN-rand was proposed by [PERSON] in 2014 [47] where all the words are randomly initialized and then modified during training. 4. CNN-static: presented also by [PERSON] in 2014, the CNN static includes a pre-training vector model from word2 vec. All words, including randomly initialized unknown words, are kept static, and only the other parameters are learned in the model. 5. CNN-non-static: Similar to CNN-static, the CNN-non-static includes a fine-tuned vector pre-trained for each task. 6. PBAL: See Table 6 for experimental parameter settings. The experimental results in test dataset 1 are summarized in Table 7. The F1 value of the proposed method was found to be higher compared with other benchmark methods. This suggests the effectiveness of personality and integrated learning in improving the emotional classification effect. Users with the same personality type are likely to be consistent with their expressions, and the sentiment classifiers trained towards a specific personality set are more precise than the general sentiment classifiers. The use of integrated learning can also make less-used personality-related \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline wordvector dimension & 128 \\ batch\_size & 100 \\ learning\_rate & 0.01 \\ dropout\_prob & 0.5 \\ num\_epochs & 750 \\ hide layer nodes & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F Score** \\ \hline
**Direct sum** & 96.58 & 97.00 & 96.78 \\
**Weighted sum** & 96.91 & 97.00 & 96.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of integration methods. features more effective while improving the classification with the use of multiple classifiers. At the same time, we applied PBAL to test dataset 2. As shown in Table 8, the results are consistent with the experimental conclusions on test dataset 7. In order to further study the influence of each personality on the final result of sentiment classification, different personality characteristics were combined and compared with the results of PBAL method. The experimental results are shown in Table 9. PBAL-HC indicates that the HC personality is not considered, PBAL-HA indicates that HA personality is not considered, PBAL-LA indicates that the LA personality is not considered, PBAL-HE indicates that HE personality is not considered, and PBAL-LE indicates that LE personality is not considered. Comparing the results shown in Tables 9 and 10, the F1 value of the PBAL method is the highest. This suggests that in the ensemble learning process, the result of the basic emotion classifier without any character affects the final classification quality. Each personality contributes to the final classification effect, which means that the training of personality-based sentiment classifiers should make full use of the text collection of the various personality. Considering personality characteristics as comprehensively as possible would improve the performance of sentiment analysis.