## 4 Experiments and Results
### Results  
In this section, we first present our classification results in all the measures mentioned above. Then we analyze the misclassification lines that were incorrectly classified by the modes. Lastly, we discuss our findings and limitations. Table 4, below, presents the results of the thematic analysis of concordance lines using both long short-term memory (LSTM) and recurrent neural network (RNN). The result shows the high accuracy of using LSTM, reaching 84%, in comparison to the 79% accuracy achieved using RNN  
Table 5, below, shows the results of the automatic thematic analysis of concordance lines. The results show a comparison between both LSTM and RNN for the four identified themes. We can clearly see that LSTM outperforms RNN within each theme, with regard to the accuracy of classifying the concordance lines. The lines are categorized into four themes: (1) Evaluation of the Syrian situation, (2) International stance, (3) Representation of the UK and USA together, and (4) Local context of the UK. The obtained accuracy was tested on unseen concordance lines. Using the LSTM, the \"Evaluation of the Syrian situation\" theme achieved 100% recall without misclassifying any lines. This is the same result achieved by RNN. Next, the \"Representation of the UK and USA together\" theme achieved the second-highest recall of 94%, with only three lines having been misclassified. RNN achieved a very close result, reaching a recall of 93%. Furthermore, the \"International stance\" theme achieved a low recall of 69%, with 16 lines having been misclassified. RNN achieved the worst result, reaching a recall of 44%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Measures** & **LSTM** & **RNN** \\ \hline Precision & 0.84 & 0.79 \\ \hline Recall & 0.84 & 0.79 \\ \hline F1 score & 0.83 & 0.78 \\ \hline Accuracy & 0.84 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of using deep learning modes LSTM and RNN in thematic analysis of concordance lines. In terms of precision, the \"Local context of the UK\", \"Representation of the UK and USA together\", and \"Evaluation of the Syrian situation\" themes achieved high precision using LSTM, with results of 89%, 88%, and 85%, respectively. The \"International stance\" theme achieved a low precision of 72%, and 14 lines were classified as belonging to the \"Local context of the UK\" theme. Generally, RNN achieved a lower precision score than LSTM. For the themes of \"International stance\" and \"Representation of the UK and USA together\", the RNN obtained slightly better results (77% and 94%, respectively) than LSTM. However, LSTM obtained better results for the themes of \"Evaluation of the Syrian situation\" (74% in RNN) and \"Local context of the UK\" (72% in RNN). Considering the overall results in Tables 4 and 5, we can see how LSTM is more accurate, in general, for the thematic analysis than the RNN model. The confusion matrix in Table 6 shows how many items match the correct categorization and how many lines match incorrect themes. Thus, we consider the percentage of the accuracy for each theme rather than only the total amount of accuracy. Table 4, below, shows that the categorization of the lines that relate to the first and third theme have the highest correct categorization among the themes. The fourth theme, \"Local context of the UK\", comes after the first and third themes in the accuracy of categorizing the lines. However, the \"International stance\" theme has the lowest accuracy of categorization. In this section, we discuss how the lines are categorized in each theme and the instances of incorrect categorization, which highlights the effectiveness and issues of the proposed methodology. As shown in the table above, the theme of evaluating the Syrian attack was the most accurate theme, which matches the exact categorization. All of the categorized lines meet the original classification. Thus, for this theme, there is no discussion of the incorrect instances.