Sentiment Reasoning for Healthcare (2407.21054v4)

Abstract: Transparency in AI healthcare decision-making is crucial. By incorporating rationales to explain reason for each predicted label, users could understand LLMs' reasoning to make better decision. In this work, we introduce a new task - Sentiment Reasoning - for both speech and text modalities, and our proposed multimodal multitask framework and the world's largest multimodal sentiment analysis dataset. Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts both the sentiment label and generates the rationale behind it based on the input transcript. Our study conducted on both human transcripts and Automatic Speech Recognition (ASR) transcripts shows that Sentiment Reasoning helps improve model transparency by providing rationale for model prediction with quality semantically comparable to humans while also improving model's classification performance (+2% increase in both accuracy and macro-F1) via rationale-augmented fine-tuning. Also, no significant difference in the semantic quality of generated rationales between human and ASR transcripts. All code, data (five languages - Vietnamese, English, Chinese, German, and French) and models are published online: https://github.com/leduckhai/Sentiment-Reasoning

• The paper introduces a novel sentiment reasoning task that enables models to articulate the rationale behind sentiment classifications in complex healthcare contexts.
• The paper designs a multimodal framework and publishes the MultiMed-SA dataset, combining speech and text data to benchmark rationale-augmented sentiment analysis.
• The paper demonstrates that rationale-augmented training with models like phoBERT significantly improves sentiment classification accuracy and trustworthiness in healthcare applications.

Sentiment Reasoning for Healthcare

The paper "Sentiment Reasoning for Healthcare" by Le-Duc et al. presents a pioneering examination of sentiment analysis within the healthcare domain, specifically focusing on the integration of reasoning tasks to improve the accuracy and depth of sentiment classification. This research introduces "Sentiment Reasoning" as a task designed to enhance the capability of LLMs to process and interpret human emotions in complex and nuanced contexts, both in speech and text modalities.

The authors propose a novel multimodal framework alongside a publicly available dataset, "MultiMed-SA," which supports Vietnamese and English-translated data. The primary aim is to improve the transparency and trustworthiness of AI systems in healthcare settings, where errors can have grave consequences.

Key Contributions and Findings

1. Introduction of Sentiment Reasoning Task: The authors introduce a new sentiment reasoning task tailored for both speech and text modalities. This task not only identifies sentiment polarity but also requires the model to understand and articulate the rationale behind the sentiment classification. This ability to reason is crucial for environments like healthcare, where decisions often rely on the nuanced understanding of emotional contexts.
2. Dataset and Framework: The introduction of the MultiMed-SA dataset marks a significant development, being the first of its kind in combining human transcript data and automatic speech recognition (ASR) conditions within the sentiment reasoning task. This resource is expected to serve as a benchmark for future studies and applications, facilitating comprehensive research and development in multimodal sentiment analysis.
3. Empirical Evaluation: The paper conducted empirical evaluations using state-of-the-art models such as phoBERT and ViHealthBERT for Vietnamese language processing. Results showed that rationale-augmented training notably improves model performance, emphasizing the importance of integrating reasoning capabilities into sentiment classification tasks.
4. Divergence in Rationale Semantics and Vocabulary: It was observed that the vocabulary of generated rationales differs significantly from human-generated rationales. Nevertheless, the semantics remain similar, suggesting that models trained with augmented rationale successfully align with human-like reasoning processes despite linguistic differences.

Experimental Setups

The authors employed a robust experimental setup, involving the use of cutting-edge models pre-trained on Vietnamese data. These models include encoder-only models like phoBERT, as well as generative models such as BARTpho and ViT5. They explored the effectiveness of rationale-augmented training through multitask learning and post-thinking strategies, which yielded statistically significant improvements in sentiment classification accuracy.

Implications and Future Directions

The integration of sentiment reasoning capabilities into AI models is particularly relevant for healthcare, where understanding patient emotions and sentiments can enhance patient care outcomes. This research lays the groundwork for more explainable AI in healthcare, promoting trust among healthcare practitioners and patients alike.

Future research could explore expanding this approach to other languages and domains, improving end-to-end sentiment analysis in low-resource settings. Additionally, there is a potential to advance towards more seamless integration of ASR and LLMs, fostering real-time and contextually-aware sentiment analysis systems.

Conclusion

In conclusion, the paper presents a comprehensive solution to sentiment analysis challenges in healthcare. By integrating reasoning capabilities, the paper offers a nuanced approach to sentiment detection, crucial for the healthcare domain's demands. The contribution of a novel dataset and framework paves the way for future advancements in this field, with the potential to significantly impact practical applications in healthcare communication and patient interaction systems. The authors provide all code and models online, inviting further research and collaboration in this evolving domain.