## 4 Results
### Results  
The results of various preprocessing procedures on different ML algorithms are presented below in Table 7. A detailed description of the performance for each algorithm follows. The results indicate that different preprocessing techniques improved the performance of each algorithm. Naive Bayes (Kernel) saw a 1.97% improvement with the combination of LEM and STEM. GLM improved by 2.90% with LEM and WR, while XGBoost saw a 3.85% increase with the same combination. The Vote algorithm's F1 average increased by 2.79% with LEM, WR, and WE, and Adaboost achieved a 3.01% improvement with LEM and WR. In Figure 9 below is the chart illustrating the comparison between the initial and best F1 scores after preprocessing for each algorithm. Figure 8: RapidMiner process—model training and testing. The Naive Bayes (kernel) algorithm demonstrates varied improvements across different preprocessing procedures. The initial dataset starts with an F1 average of 53.18%. Lemmatization (LEM) shows a minor increase to 53.24%, while Word Replacement (WR) and Word Exclusion (WE) slightly reduce the performance to 52.23% and 52.71%, respectively. Stemming (STEM) improves it marginally to 53.28%. The combination of LEM and WR leads to a notable increase to 53.66%, while LEM combined with WE shows a decrease to 51.88%. LEM and STEM together achieve the highest individual preprocessing improvement at 55.15%. Other combinations, such as WR + WE and WR + STEM, do not significantly enhance performance. The triple preprocessing combination of LEM + WR + WE further boosts the F1 average to 55.08%. Overall, the combination of multiple preprocessing techniques yields the best results for Naive Bayes (kernel). GLM initially performs with an F1 average of 51.66%. Lemmatization significantly improves this to 53.76%. WR results in a slight decrease to 51.25%, and WE further reduce the score to 49.55%. STEM brings the score close to the baseline at 50.44%. The combination of LEM + WR yields the highest improvement at 54.56%. Interestingly, combining LEM with WE or STEM does not show significant improvements, with scores of 51.80% and 53.26%, respectively. The triple combination of LEM + WR + WE results in a moderate increase to 51.72%, whereas the inclusion of STEM in various combinations such as WR + WE + STEM and LEM + WR + WE + STEM achieves moderate improvements. Therefore, GLM benefits most notably from the LEM + WR combination. XGBoost begins with an F1 average of 49.69%. Lemmatization improves the performance to 52.16%, indicating a substantial benefit. WR and WE alone result in slight improvements, with scores of 52.01% and 50.70%, respectively. STEM also shows a moderate enhancement to 51.80%. The LEM + WR combination achieves the highest individual improvement at 53.54%. However, combining LEM with WE or STEM shows diminished returns, with scores of 48.84% and 51.54%, respectively. The combination of WR + STEM yields the highest performance at 52.35%. The multiple combinations such as LEM + WR + WE or LEM + WR + STEM do not significantly outperform the best individual combinations. Consequently, XGBoost shows the most improvement with the LEM + WR preprocessing combination. The Vote algorithm starts with an F1 average of 51.59%. Lemmatization increases the score to 53.86%, while WR and WE result in slight reductions to 51.23% and 51.22%, respectively. STEM shows a moderate improvement to 52.18%. The LEM + WR combination results in the highest improvement at 54.16%. Other combinations, such as LEM + WE and LEM + STEM, show varying levels of improvement,