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• W2912771644 abstract "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." @default.
• W2912771644 created "2019-02-21" @default.
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• W2912771644 date "2018-12-01" @default.
• W2912771644 modified "2023-09-25" @default.
• W2912771644 title "Explainable Sentiment Analysis with Applications in Medicine" @default.
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• W2912771644 doi "https://doi.org/10.1109/bibm.2018.8621359" @default.
• W2912771644 hasPublicationYear "2018" @default.
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