## 4 Results and Discussion
The explainability framework effectively visualizes the decision-making process, demonstrating how word-level importance scores can provide transparency in NLP-based text classification models. Figure 23 illustrates a LIME-based interpretability analysis for a text classification model designed to predict whether a given sentence belongs to Class 0 (non-violent) or Class 1 (violent). The prediction probability bar on the left shows that the model assigns a significantly higher probability to Class 1, indicating that the input sentence is classified as violent speech. The right-hand side of the visualization presents the word-level importance scores, where the words highlighted in orange contribute positively to the Class 1 (violent) prediction, while the words in blue negatively impact this classification, pulling the model's decision towards Class 0 (non-violent). The most influential positive contributors to Class 1 include \"you\" (+0.08), \"come\" (+0.05), and \"will\" (+0.03). The phrase \"I will come to you!\" is heavily associated with violent intent, likely due to its threatening or confrontational tone. The words \"to\" (+0.03), \"I\" (+0.03), and \"indifference\" (+0.03) also contribute to the classification, but their individual impact is relatively minor. In contrast, words such as \"is\" (+0.02), \"an\" (+0.02), \"How\" (+0.01), and \"this\" (+0.00) have a neutral or slightly negative effect, meaning that they shift the classification marginally towards Class 0 (non-violent). However, their influence remains insufficient to alter the overall decision. The highlighted text structure suggests that the model strongly associates direct personal references (\"you\") and action-oriented verbs (\"come\", \"will\") with violent intent, reinforcing its Class 1 prediction. The LIME-based explanation effectively showcases  
Figure 22: Sentence 1 LIME (Local Interpretable Model-agnostic Explanations) graph. the model's decision-making process, providing transparency on how specific linguistic elements influence classification. In Figure 24, the visualization presents a LIME-based interpretability analysis for a text classification model predicting whether a given sentence belongs to Class 0 (non-violent) or Class 1 (violent). The prediction probability bar on the left indicates that the model assigns a significantly higher probability to Class 1, suggesting that the input sentence is classified as violent speech. The word-level contribution analysis on the right further explains how the individual words influence this classification. The words highlighted in orange contribute positively to the Class 1 (violent speech) prediction, while the words in blue contribute negatively, pulling the classification towards Class 0 (non-violent). The strongest positive contributors to Class 1 include \"you\" (+0.05), \"made\" (+0.05), \"him\" (+0.04), and \"father\" (+0.03). The phrase \"you made him sick\" appears to be the primary driver of the violent classification, as it conveys blame or accusation, which may be associated with hostility or conflict in the model's learned patterns. Conversely, words such as \"was\" (+0.02), \"my\" (+0.01), \"the\" (+0.01), and \"It\" (+0.00) exert weak negative effects, slightly pulling the classification towards Class 0 (non-violent). However, their impact is minimal compared with the dominant Class 1 indicators. The results indicate that the model strongly associates direct personal references (\"you\") and accusatory action verbs (\"made\") with aggressive or confrontational speech. The LIME-based explanation effectively highlights how the model determines its classification, providing insight on its decision-making process. In Figure 25, the visualization presents a LIME-based interpretability analysis for a text classification model, explaining its decision-making process in classifying a given sentence as Class 0 (non-violent) or Class 1 (violent).