## 4 Experiments and Results
### Results
The second most accurate theme was the \"Representation of the UK and USA together\" theme, which had three incorrect classifications. For example:  
1. [PERSON] administered a diplomatic slap in the face to Britain following [PERSON]'s withdrawal of military support for intervention in Syria. In the experiment, Example 1 was categorized under the theme of \"International stance\", while it originally related to \"Representation of the UK and USA together\". Categorizing such examples is difficult, as this theme seems to be in the blurred area between \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{Predicted Label} \\ \cline{2-5}  & Evaluation of the & \begin{tabular}{c} Evaluation of the \\ Syrian Situation \\ \end{tabular} & \begin{tabular}{c} International Stance \\ \end{tabular} & \begin{tabular}{c} Representation of the \\ UK and USA Together \\ \end{tabular} & \
\begin{tabular}{c} Local Context of \\ the UK \\ \end{tabular} \\ \cline{2-5}  & Evaluation of the & 51 & 0 & 0 & 0 \\ \cline{2-5}  &
\begin{tabular}{c} International stance \\ \end{tabular} & 6 & 36 & 5 & 5 \\ \cline{2-5}  & Representation of the & 0 & 3 & 51 & 0 \\ \cline{2-5}  & UK and USA together & & & & \\ \cline{2-5}  & Local context of the UK & 3 & 11 & 2 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix for thematic analyses using LSTM model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Themes**} & \multicolumn{2}{c}{**Evaluation of the Syrian**} & \multirow{2}{*}{**International Stance**} & \multirow{2}{*}{
\begin{tabular}{c} **Representation of the UK** \\ **and USA Together** \\ \end{tabular} } & \multirow{2}{*}{**Local Context of the UK**} \\ \cline{2-5}  & & & & & & & & \\ \hline
**Measures** & **LSTM** & **RNN** & **LSTM** & **RNN** & **LSTM** & **RNN** & **LSTM** & **RNN** \\ \hline Precision & 0.85 & 0.74 & 0.72 & 0.77 & 0.88 & 0.94 & 0.89 & 0.72 \\ \hline Recall & 1.00 & 1.00 & 0.69 & 0.44 & 0.94 & 0.93 & 0.71 & 0.79 \\ \hline F1 score & 0.92 & 0.85 & 0.71 & 0.56 & 0.91 & 0.93 & 0.79 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the text automatic thematic analysis over four themes using LSTM and RNN. more than one theme (this point will be expanded below, with the consideration of the other themes). This example and the other lines in the corpus show the high accuracy of classifying the lines of this theme, even though the algorithm missed three instances. A less-accurately categorized theme was \"Local context of the UK\". The algorithm missed the correct categorization of 16 lines. From the lexical perspective, regarding their semantic use, words such as \"UK\" and \"Britain\" were frequently used both for the representation of the local context and also from the perspective of the international stance. For example:  
1. International community needs to ask: would Assad utilize chemical weapons stage bring potential western military intervention international community to investigate the reasons behind the use of chemical weapons. 2. Later came comments from the USA House speaker [PERSON] backing a Syria war resolution, adding to the likelihood of Congress voting for USA action. Examples 2 and 3 were classified as a part of the UK stance, while they were originally categorized in the theme of \"International stance\". Lexically, words such as \"international\", \"community\", \"internationally\", and \"prohibited\" were frequently used with the \"International stance\" theme. In Example 4, the algorithm considered terms such as \"voting\" and \"resolution\" as strongly connected to the local context, while the semantic function of the whole line suggested the connection of the example to the \"International stance\" theme. The least accurate theme in the categorization was the \"International stance\" theme. Table 5, above, shows that the \"International stance\" theme had various incorrect classifications for the different themes.