## 4 Results
### Results
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Datasets** & **Naïve Bayes (Kernel)** & **GLM** & **XGBoost** & **Vote** & **Adaboost** \\ \hline Initial Dataset & 53.18\% & 51.66\% & 49.69\% & 51.59\% & 47.16\% \\ LEM & 53.24\% & 53.76\% & 52.16\% & **53.86\%** & 47.71\% \\ WR & 52.23\% & 51.25\% & 52.01\% & 51.23\% & 47.26\% \\ WE & 52.71\% & 49.55\% & 50.70\% & 51.22\% & 47.49\% \\ STEM & 53.28\% & 50.44\% & 51.80\% & 52.18\% & 46.36\% \\ LEM + WR & 53.66\% & **54.56\%** & **53.54\%** & **54.16\%** & **51.17\%** \\ LEM + WE & 51.88\% & 51.80\% & 48.84\% & 50.13\% & 49.06\% \\ LEM + STEM & **55.15\%** & 53.26\% & 51.54\% & 52.16\% & 48.29\% \\ WR + WE & 50.19\% & 49.49\% & 50.24\% & 53.30\% & 47.10\% \\ WR + STEM & 51.53\% & 51.65\% & 52.35\% & 50.77\% & 47.93\% \\ WE + STEM & **53.87\%** & 49.75\% & 50.67\% & 52.70\% & 46.59\% \\ LEM + WR + WE & **55.08\%** & 51.72\% & 50.41\% & **54.38\%** & 48.66\% \\ LEM + WR + STEM & 51.70\% & 51.48\% & **52.96\%** & 49.48\% & 46.50\% \\ LEM + WE + STEM & **54.79\%** & 52.86\% & 50.11\% & **53.86\%** & 49.83\% \\ WR + WE + STEM & 49.82\% & 51.02\% & 49.49\% & 50.63\% & 46.84\% \\ LEM + WR + WE + STEM & **54.29\%** & 53.42\% & 51.03\% & 52.10\% & 49.21\% \\ \hline \end{tabular}
\begin{tabular}{l c c c c} \hline
**LEM + LEM** & **W** = Normal Replacement, WE = Word Exclusion, STEM = Stemming. Bold values highlight the top-performing results. \\ \end{tabular}
\end{table} 
Table 7: ML experiment results by preprocessing task. with scores of 50.13% and 52.16%, respectively. The triple combination of LEM + WR + WE further increase the F1 average to 54.38%, indicating the benefit of multiple preprocessing techniques. Therefore, Vote benefits significantly from the LEM + WR combination and its variations. Adaboost starts with the lowest initial F1 average of 47.16%. Lemmatization shows a slight improvement to 47.71%. WR and WE result in marginal changes, with scores of 47.26% and 47.49%, respectively. STEM reduces performance to 46.36%. The LEM + WR combination provides a notable improvement to 51.17%, which is the highest among the preprocessing techniques. Other combinations such as LEM + WE and LEM + STEM do not significantly enhance performance, showing scores of 49.06% and 48.29%, respectively. The combination of LEM + WR + WE improve the F1 average to 48.66%, indicating a slight benefit from multiple preprocessing techniques. Adaboost benefits the most from the LEM + WR preprocessing combination. In summary, Lemmatization generally proves to be the most effective single preprocessing technique across the algorithms, consistently improving performance. The combination of LEM with WR often results in the highest improvements, particularly for Naive Bayes (kernel), GLM, and XGBoost. The Vote algorithm also benefits significantly from this combination, while Adaboost shows the most notable improvement with the LEM + WR combination, indicating the robustness of ensemble methods. GLM and XGBoost generally performed well, particularly in dataset combinations with lemmatization and corrections, underscoring their effectiveness in handling structured and normalized text data. It is obvious that combining multiple preprocessing techniques tends to yield better results, highlighting the importance of preprocessing in enhancing ML algorithm performance. For example, datasets with LEM + WR and LEM + WR + WE showed higher performance across most of the algorithms, suggesting that integrating multiple text normalization methods is beneficial. Also, stemming alone or in combination with other techniques (e.g., LEM + STEM) showed moderate improvements but in most cases did not outperform the combinations involving lemmatization. These findings underscore the necessity to tailor preprocessing strategies to specific algorithms to maximize their effectiveness. Table 8 below presents the confusion matrix illustrating the performance of the Naive Bayes (Kernel) algorithm, identified as the top-performing model in this analysis.