## 5 Discussion  
The analysis revealed that the Naive Bayes (Kernel) algorithm, particularly when applied to dataset combinations involving lemmatization and its corrections, outperformed other models with an F1 average of up to 55.15%. The addition of lemmatization and subsequent corrections consistently improved the performance across most algorithms, indicating that normalizing the text body by reducing words to their base forms and correcting any lemmatization errors significantly improves the model's understanding of the corpus. The combination of various preprocessing techniques generally yielded better results. Furthermore, ensemble methods like the Vote algorithm also showed robust performance across multiple dataset configurations, reinforcing the value of integrating multiple preprocessing steps. These findings highlight the importance of comprehensive text preprocessing, showing that integrating multiple techniques generally enhances the accuracy and reliability of topic identification models. It is also important to note that  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **Class A** & **Class B** & **Class C** & **Class D** & **Class Precision** \\ \hline Class A & 99 & 44 & 17 & 6 & 59.64\% \\ Class B & 39 & 205 & 60 & 44 & 57.75\% \\ Class C & 37 & 95 & 148 & 27 & 48.21\% \\ Class D & 6 & 51 & 15 & 88 & 57.52\% \\ \hline Class Recall & 54.70\% & 52.84\% & 61.67\% & 51.16\% & \\ \hline \end{tabular}
\end{table}
Table 8: Confusion Matrix of the Top Performing Algorithm.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Paper** & **Data** & **Classes** & **ML** & **Results** \\ \hline
[57] & 4779 Greek Twitter posts & \
\begin{tabular}{l} offensive 3 \\ (imbalanced) \\ \end{tabular} &
\begin{tabular}{l} LSTM and GRU with \\ Attention \\ \end{tabular} & 89\% F1-score \\ \hline
[58] &
\begin{tabular}{l} 11,156 Greek teachers’ \\ reviews \\ \end{tabular} & 5 (imbalanced) & REPTree & 57.96\% Accuracy \\ \hline
[78] &
\begin{tabular}{l} 727 English interviews of \\ cancer patients \\ \end{tabular} & 3 (balanced) & Decision Tree & 75.38\% Accuracy \\ \hline
[79] & \begin{tabular}{l} 339 English interviews for \\ mental health \\ \end{tabular} & 3 (imbalanced) & Transformers & 77\% Accuracy \\ \hline This paper & \
\begin{tabular}{l} 12 Greek interviews \\ for ERT \\ \end{tabular} & 4 (imbalanced) & [PERSON] (Kernel) &
\begin{tabular}{l} 55.15\% F1-score, \\ 61.67\% Recall \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparison with related work.  
in some cases, the results did not improve after the application of SMOTE. From the comparative analysis of the research results with relevant literature (Table 9), it is evident that various research approaches using ML and DL algorithms have reported superior performance metrics. However, this research holds significant value. It introduces a smaller yet novel dataset, unlike others that rely on publicly available datasets.  
The findings suggest that linguistic complexity plays a significant role in topic modeling, particularly in the context of a morphologically rich and low-resource language such as Modern Greek. The highest classification accuracy achieved (55.15%) highlights the importance of preprocessing, especially the combination of lemmatization and stemming. The proposed framework could support the systematic analysis of qualitative data collected during ERT, offering a replicable approach for identifying recurring themes in parental narratives. This approach could also assist in data-driven decision-making by providing structured insights into the use of ICT in education, potentially informing inclusive strategies and contributing to the development of more equitable remote learning environments.