## 4 Results and Discussion
While T5 + GNN shows a poor overall performance with an accuracy of 51.43%, it is effective in detecting positive classes with a recall rate of 100%. However, the RAG algorithm offers a more balanced text classification performance with 92.38% accuracy and 95.10% precision. In particular, the combination of RAG + ECE shows a superior overall performance with 97.67% accuracy, precision, and recall rates, indicating that it is one of the strongest alternatives among the other models. These results suggest that each algorithm should be optimized for specific data types and classification problems, and that balancing accuracy, precision, and recall metrics is a critical success factor. _Implementation of the Explainable Artificial Intelligence LIME Algorithm for the RAG-ECE Model_  
LIME (Local Interpretable Model-agnostic Explanations) is an interpretable and model-agnostic method developed to explain the predictions made by any machine learning model. It is particularly useful in interpreting the predictions of complex models, enabling users to better understand the decision-making mechanisms of the model. By doing so, LIME aims to enhance the reliability of model outcomes and make the behavior of the model more transparent [24]. Table 3 shows six randomly selected sample sentences on which the LIME algorithm was run. Figure 22 demonstrates the interpretability analysis of a text classification model, specifically for predicting whether a given sentence belongs to Class 0 (non-violent) or \begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Sentence (Turkish–English Original Dataset)** \\ \hline \hline Sentence-1 & Bu ne bicim tedavi seni dava edecgim. \\ \hline \hline Sentence-2 & How is this an indifference, I will come to you. \\ \hline \hline Sentence-3 & It was better when my father came to the hospital, you made him sick. \\ \hline \hline Sentence-4 & What can I do to raise my mother’s morale. \\ \hline \hline Sentence-5 & Bunu senin yanna brakmayacagim, hazir ol. \\ \hline \hline Sentence-6 & Tedavi srasimda dikkat etmem gereken birsey var mu? \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sample sentences randomly selected from the dataset evaluated using the LIME algorithm. Class 1 (violent). The prediction probability bar on the left indicates that the model assigns a higher probability to Class 1, suggesting that the input text is classified as violent. The analysis on the right further elaborates on the word-level contributions to this classification decision by highlighting their respective importance scores. In this LIME-based explainability framework, the words highlighted in orange exert a positive influence on Class 1, strengthening the model's prediction that the sentence is violent, whereas the words in blue contribute negatively, pulling the classification towards Class 0 (non-violent). The most dominant positive contributors include \"seni\" (you, +0.07), \"edecegim\" (1 will do, +0.06), and \"dava\" (lawsuit, +0.05), which indicate a legal threat and contribute significantly to the model's decision towards Class 1. Additionally, the word \"Bu\" (this, +0.07) also appears to have a positive effect, likely due to its contextual association with assertive or accusatory language. On the other hand, words such as \"ne\" (what, +0.05), \"tedavi\" (treatment, +0.01), and \"bicim\" (form/style, +0.01) provide a weak counterbalance by slightly pulling the classification towards Class 0 (non-violent). However, their influence remains marginal compared with the dominant Class 1 indicators, suggesting that the model does not strongly associate them with non-violent speech in this context. The overall classification decision aligns with the semantic structure of the sentence, as the presence of legally threatening phrases (e.g., \"seni dava edecegim\"-\"I will sue you\") strongly influences the model's prediction.