## 3 Materials and Methods
### Dataset
#### 3.2.2 Versions of Datasets Creation--Combinations of Preprocessing Techniques  
Screening the \"Initial Dataset\" results, it was observed that the initial dataset did not have satisfactory results due to the presence of residual noise. The findings underscore the critical need for more preprocessing measures to improve the dataset's quality and the subsequent classification accuracy. In order to achieve better results, lemmatization was performed on the corpus. Lemmatization process reduced word variants to their dictionary form, or lemma, by employing vocabulary and morphological analysis utilizing the elcorenewslg-3.7.0 model from spaCy [75]. This model was selected for its extensive vocabulary and superior linguistic accuracy. The model was integrated in the Python environment by installing spaCy and downloading the specific language model, enabling to standardize Greek words to their lemma forms efficiently. This methodology enhances text analysis by aligning with modern NLP practices and ensuring precise handling of Greek linguistic features. After lemmatization, it was observed that not all words were correctly lemmatized, necessitating further corrections. To address this, a Python script was developed to refine the dataset more effectively. This custom script completed the lemmatization process and replaced words that confused the algorithm. For \begin{table}
\begin{tabular}{l c} \hline
**Word** & **Count** \\ \hline _evyququququ_ (“thanks”) & 9 \\ _xxa_ (“and”) & 8 \\ _qx_ (“no”) & 7 \\ _evy_ (“I”) & 6 \\ _avr_ (“that’s all”) & 6 \\ _raqxcoooo_ (“you are welcome”) & 4 \\ \hline \end{tabular}
\end{table} 
Table 5: Word occurrences in topic “Not Defined”. Figure 2: Preparation and normalization steps for data import and preprocessing. instance, verbs in different tens were standardized to the infinitive in the present tense or converted to the first-person singular. Similarly, nouns, adjectives, and particles were adjusted to the non-ininative singular in the masculine gender. Examples of these adjustments include \"\(\varoxi\vgamma\)\" to \"\(\varoxi\vgamma\)\" (\"I was saying\" to \"I say\"), \"\(\varoxi\vgamma\)\" to \"\(\varoxi\vgamma\)\" (\"of the lesson\" to \"lessson\"), \"\(\varoxi\vgamma\)\" to \"\(\varoxi\vgamma\)\" (\"tired\" plural to \"tired\" singular), and \"\(\varoxi\vgamma\)\" to \"\(\varoxi\vgamma\)\" (\"teachers\" to \"teacher\"). After this, one more preprocessing task was attempted to improve the model's accuracy by excluding some words that appeared across all four topics, as presented in Section 3.2.1 in Table 4, and created ambiguity that reduced the accuracy of the ML model. These words were \"\(\varoxi\vgamma\)\" (\"child\"), \"\(\varoxi\vgamma\)\", \"\(\varoxi\vgamma\)\" (\"teacher\"), \"\(\varoxi\vgamma\)\" (\"school\"), \"\(\varoxi\vgamma\)\" (\"lessson\"), \"\(\varoxi\vgamma\)\" (\"hours\"), \"\(\varoxi\vgamma\)\" (\"times\"), \"\(\varoxi\vgamma\)\" (\"could\"), \"\(\varoxi\vgamma\)\" (\"house\"), \"\(\varoxi\vgamma\)\" (\"I am\"), \"\(\varoxi\vgamma\)\" (\"he\"), \"\(\varoxi\vgamma\)\" (\"why\"), \"\(\varoxi\vgamma\)\" (\"other\"), \"\(\varoxi\vgamma\)\" (\"lg\"), \"\(\varoxi\vgamma\)\" (\"many\"), \"\(\varoxi\vgamma\)\" (\"someone\"), \"\(\varoxi\vgamma\)\" (\"must\"), \"\(\varoxi\vgamma\)\" (\"of course\"), \"\(\varoxi\vgamma\)\" (\"that is\" or \"i.e.,\"), \"\(\varoxi\vgamma\)\" (\"okay\"), \"\(\varoxi\vgamma\)\" (\"especially\"), \"\(\varoxi\vgamma\)\" (\"so\"), \"\(\varoxi\vgamma\)\" (\"exist\"), \"\(\varoxi\vgamma\)\" (\"ERT\"). As a final preprocessing step, was attempted to perform stemming using the Greek Stemmer tool, publicly available in a GitHub repository [76]. This step aimed to further refine the text corpus by reducing words to their root forms, thereby enhancing the model's ability to accurately interpret and classify the data. From the processes described above, Table 6 details the dataset versions corresponding to each processing technique.