## 4 Experimental Results and Discussions
### Model Interpretability and Explainability
#### 4.2.2 SHAP Analysis  
SHAP (SHapley Additive exPlanations) analysis is a method that explains the contribution of each word in a sentence to the model's classification output by assigning numerical values and color-coded visualizations [2]. This method is based on Shapley values and quantifies how much each feature (word) contributes to the final prediction. The base  
Figure 8: Attention heatmap visualization for a positive sentence.  
Figure 10: Attention heatmap visualization for a neutral sentence.  
Figure 9: Attention heatmap visualization for a negative sentence.  
value represents the model's general tendency when no specific input is provided, serving as a reference point for classification. The f(output) value indicates the final prediction computed for a given input, incorporating the influence of individual words. The difference between base value and f(output) reflects the cumulative effect of the words in the input on the final classification. In SHAP visualizations, words contributing to a positive sentiment classification are highlighted in red/pink tones, whereas words pushing the classification towards a negative sentiment are shown in blue tones. The length of each bar represents the magnitude of a word's impact on the prediction, while the direction indicates whether it positively or negatively influences the classification. In this study, output 0 represents negative sentiment, output 1 represents neutral sentiment, and output 2 corresponds to positive sentiment.  
In Figure 11, in the positive sentence \"Evin dekorasyonunda renk uyumu kusursuzdu\", the f(output) value (\(-0.605057\)) indicates that the model's prediction is directed toward the positive class. The word \"kusursuz\" makes a strong contribution to the positive sentiment, as indicated by the red tone and the length of the bar. Similarly, \"uyumu\" also provides a meaningful contribution. The difference between the base value (\(-1.74495\)) and f(output) demonstrates how the input words collectively influenced the model to classify the sentence as positive. Also, in the negative sentence \"Aldiktan 3 gun sonra bozulan fiyasko bir urun\", the f(output) value (\(1.56536\)) suggests that the model has classified the sentence as negative. The word \"bozulan\" is the most influential in determining the negative classification, as shown by the blue color and the significant bar length. Additionally, \"fiyasko\" reinforces the negative sentiment prediction. The difference between the base value (\(-1.78928\)) and f(output) highlights the collective impact of these words in shifting the classification toward the negative class. Finally, in the neutral sentence \"Siparis belirtiel narih araligunda adresime teslim edildi\", the f(output) value (\(-1.51784\)) suggests that the model's classification remains close to neutral. The phrase \"teslim edildi\" contributes the most to the neutral classification, as indicated by the bar length. Additionally, \"belirtilen tarih\" plays a supporting role in maintaining the neutral prediction. The difference between the base value and f(output) quantifies the extent to which the words influence the sentence's classification toward neutrality.