# LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model
## 4 Experimental Results and Analysis
### Performance Measurement
For the training of the models, maximum length of the sentence was set to 60, and zero filling operation was performed with the same number of sentences. Table 6 and Figures 7 and 8 show the analysis results of the sentiment classification results in comparison with the SVM, CNN, and LSTM models. The precision, recall, and F1 score of the positive sentiment class are highlighted by \(\text{P}_{\text{pos}}\), \(\text{R}_{\text{pos}}\), and \(\text{F}_{\text{1 pos}}\). For the negative sentiment class, precision was highlighted as \(\text{P}_{\text{neg}}\), recall as \(\text{R}_{\text{neg}}\), and F1 score as \(\text{F}_{\text{1 neg}}\), respectively. The F1 score is the weighted average of precision and recall. Hence, this score considers both false positives and false negatives. Intuitively, it is not as easy to understand as the accuracy, but F1 is more commonly used than accuracy. It can be seen that the deep learning models performed better in sentiment classification than SVM, which is a traditional method. The F1 score calculated using following Equation (12). \[F1=\frac{2\times precision\times recall}{precision+recall} \tag{12}\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Positive**} & \multicolumn{3}{c}{**Negative**} & \multirow{2}{*}{**Marco\_F1**} \\ \cline{2-2} \cline{4-7}  & \(\text{P}_{\text{pos}}\) & \(\text{R}_{\text{pos}}\) & \(\text{F1}_{\text{pos}}\) & \(\text{P}_{\text{neg}}\) & \(\text{R}_{\text{neg}}\) & \(\text{F1}_{\text{neg}}\) & \\ \hline SVM & 84.80 & 80.70 & 82.22 & 82.10 & 83.50 & 82.79 & 82.51 \\ CNN & 86.00 & 85.60 & 85.80 & 85.17 & 85.82 & 85.49 & 85.65 \\ LSTM & 85.10 & 82.50 & 83.78 & 84.30 & 86.10 & 85.19 & 84.49 \\ Our model & 94.0 & 18.0 & 30.0 & 55.0 & 99.0 & 70.0 & 58.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: TDS model classification. Figure 7: Positive sentiment class. Precision is defined as the number of true positives over the number of true positives plus the number of false positives. Recall is defined as the number of true positives over the number of true positives plus the number of false negatives. It can be seen from Figure 7 that the TDS model performs better only in the positive precision sentiment class compared with the other three models, reaching 94.0% in the TDS model and 84.8%, 86.0%, and 85.1% in the SVM, CNN, and LSTM models, respectively. Nevertheless, the TDS model reaches the lowest classification results in the recall and F1 score classes. The range of the text sentiment score was between \([-1,1]\) for all distributed samples, which selected experimental data length was between [10; 40]. Figure 8 shows the negative sentiment class classification performances of the three models compared with our model TDS. Negative recall sentiment class (Rneg) results indicated that classification of our model performed relatively high classification reach than other models, to around 99.0%, while SVM, CNN and LSTM models reached around 83.5%, 85.82% and 86.1%, respectively.