## 4 Results and Discussion
The prediction probability bar on the left shows that the model assigns a high probability to Class 0, indicating that the sentence  
Figure 23: Sentence 2 LIME graph. Figure 24: Sentence 3 LIME graph. is classified as non-violent speech with a strong confidence. The word-level contribution analysis on the right highlights how specific words influenced this classification. The words in blue contribute positively to Class 0 (non-violent prediction), while the words in orange contribute positively to Class 1 (violent prediction). The most dominant non-violent indicators include \"can\" (+0.97), \"morale\" (+0.30), \"to\" (+0.23), and \"s\" (+0.31), which strongly reinforce the classification towards Class 0. The word \"can\" is particularly influential, suggesting that the model associates it with help-seeking or constructive language, which aligns with a non-violent context. Conversely, words such as \"raise\" (+0.37), \"What\" (+0.36), \"do\" (+0.16), and \"mother\" (+0.07) exert a positive influence on Class 1, but their impact remains insufficient to shift the classification away from Class 0. This suggests that while some words may have contextual ambiguity, their overall contribution does not indicate aggression or verbal violence. The results confirm that the model effectively recognizes positive and constructive linguistic patterns, distinguishing them from aggressive or confrontational expressions. The LIME-based explanation provides a transparent view of the model's decision-making, demonstrating how word-level importance influences the text classification. In Figure 26, the visualization presents a LIME-based interpretability analysis for a text classification model that predicts 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 text is classified as violent speech. The word-level importance scores displayed in the middle provide insight on how individual words contribute to the classification decision. The words in orange contribute positively to the Class 1 (violent) prediction, whereas the words in blue have a negative impact, pulling the classification towards Class 0 (non-violent). The most dominant contributors to Class 1 include \"senin\" (yours, +0.10), \"brakmayacagim\" (I will not leave, +0.06), and \"o1\" (be, +0.04). These words are strongly associated with assertive or potentially threatening language, particularly in contexts where personal references (\"senin\") and commitment-based expressions (\"brakmayacagim\") appear. The word \"ol\" may also carry imperative connotations, further reinforcing the Class 1 classification. On the other hand, words such as \"hazir\" (ready, +0.04), \"Bunu\" (this, +0.03), and \"yanna\" (next to, +0.03) exert a marginal positive impact on Class 0, suggesting that they are less indicative of violent speech. However, their influence is insufficient to counteract the dominant Class 1 predictors. Overall, the model strongly associates the phrase \"senin brakmayacagim\" (\"I will not leave you\") with aggressive or threatening intent, leading to a Class 1 (violent speech) prediction. The LIME-based explanation effectively visualizes the  
Figure 25: Sentence 4 LIME graph. model's decision-making process, providing valuable insight on the role of specific words in shaping the classification outcomes. In Figure 27, the visualization presents a LIME-based interpretability analysis for a text classification model, explaining its decision-making process in categorizing a given sentence as 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 0, suggesting that the sentence is classified as non-violent speech with a high confidence. The word-level contribution analysis in the middle highlights the influence of individual words on the classification.