Explainable Sentiment Analysis with Applications in Medicine

By Chiara Zucco, Huizhi Liang, Giuseppe Di Fatta, Mario Cannataro. 2018

43 citations recorded by the source corpus.

Sentiment Analysis can help to extract knowledge related to opinions and emotions from user generated text information. It can be applied in medical field for patients monitoring purposes. With the availability of large datasets, deep learning algorithms have become a state of the art also for sentiment analysis. However, deep models have the drawback of not being non human-interpretable, raising various problems related to model’s interpretability. Very few work have been proposed to build models that explain their decision making process and actions. In this work, we review the current sentiment analysis approaches and existing explainable systems. Moreover, we present a critical review of explainable sentiment analysis models and discussed the insight of applying explainable sentiment analysis in the medical field.

Subfields: Artificial Intelligence

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Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “plainability in the medical ﬁelds [11], [20], [43]. They provide summaries of previous works [21] including subﬁeldspeciﬁc reviews such as [25] for chest radiograph and sentiment analysis in medicine [160], or at least set aside a section to promote awareness for the importance of interpretability i”
Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021, methodology): “In terms of post-hoc explainability, perturbation-based models that use model-agnostic explanations are common in the literature [98,99,118,120,127] and have been used in two of the proposed CDSS [118,120].”
Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond (2021, unknown): “More specifically, specific analyses have been investigated, e.g., chest radiography [128], emotion analysis in medicine [129], COVID-19 detection and classification [43], and the research encourages understanding of the importance of interpretability in the medical field [130].”