Computational approaches for mining user's opinions on the Web 2.0

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What do we need to build explainable AI systems for the medical domain? (2017, background): “…emphasize that successful explainable-AI systems need eﬀective user interfaces, fostering new strategies for presenting human understandable explanations, e.g. explanatory debugging (Kulesza et al., 2015), aﬀective computing (Picard, 1997), sentiment analysis (Maas et al., 2011; Petz et al., 2015).”
Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, background): “ML is very broad and deals with the problem of extracting features from data to solve predictive tasks, including decision support, forecasting, ranking, classifying (e.g., in cancer diagnosis), detecting anomalies (e.g., virus mutations), or sentiment analysis [12].”
A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “This type of categorization can be seen in some works (Wang et al. 2015a; Petz et al. 2015), but in this paper we address the issue muchmore comprehensive in more details with better comparison factors and discussions based on the major validated scientific works.”