# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 5 Experiments
### Ablation Study on MSIS
**Probability Discrepancy (PD)**: This component, denoted as \(\Delta P^{Y}\), assesses the clarity of sentiment cues within the candidate sequence. It ensures that elements marked with 1 in the candidate sequence effectively contribute to the sentiment classification model's decision-making process. **Inverse Probability Discrepancy (IPD)**: Represented as \(\Delta P^{Y}\), it evaluates the absence of sentiment cues within the inverse attention mask \(\bar{Y}\).This ensures elements marked with 0 in \(Y\) do not contribute significantly to sentiment interpretation, emphasizing the specificity of extracted cues. **Ratio of Cue Tokens (RCT)**: This component aims to minimize the inclusion of irrelevant tokens in the candidate sequence, promoting a concise extraction of sentiment cues. It is calculated as the proportion of 1s in \(Y^{c}\), with a higher RCT indicating a more focused extraction of sentiment cues. The results of our ablation study are summarized in Tables 9-12. Each row represents a variant of the MSIS, indicating the presence (+) or absence (-) of each component. Performance metrics include the accuracy, precision, recall, and F1 score of the sentiment cue extraction under each variant. As shown in Tables 9-12, the ablation study systematically evaluates the contribution of each component within the MSIS on the SST-2, Yelp, IMDb, and ChnSentiCorp datasets. This study offers a nuanced understanding of how each element influences the framework's ability to extract and utilize sentiment cues. Removing the PD component results in performance degradation across most metrics, particularly evident in the reduction of precision and the F1 score. This suggests that PD is crucial for identifying clear sentiment cues within the text, ensuring that the elements marked as sentiment cues in the candidate sequence contribute effectively to the decision-making process of the sentiment classification model. However, on the Chinese dataset, the performance after removing the PD component is slightly better than the overall performance with the complete MSIS. This may be attributed to the fact that the Chinese language processes characters as the smallest units, rather than words. It is important to note that while the removal of PD results in a decrease in RCT by 0.1894, the F1 score only drops by 0.0312, illustrating the effectiveness of our method. The absence of the IPD leads to a significant decrease in recall and a noticeable drop in the RCT, indicating a diminished ability to exclude non-sentiment-related tokens from being marked as sentiment cues. This highlights the IPD's role in refining the specificity of extracted cues by ensuring that elements marked with 0 in \(Y\) do not significantly contribute to sentiment interpretation. Removing RCT results in an improvement in sentiment classification performance but at the cost of a substantial increase in RCT. This implies that while the RCT component restricts the inclusion of irrelevant tokens in the candidate sequence, its absence leads to a wider selection of tokens as sentiment cues, including potentially irrelevant ones. In summary, each component of the MSIS plays a vital role in the sentiment cue extraction process. PD ensures the clarity and relevance of cues, IPD enhances the specificity of cue extraction, and RCT promotes conciseness and focus. The ablation study demonstrates the delicate balance between these components, underscoring their collective contribution to the effectiveness of the SS-SCE framework.