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Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients’ narratives

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Abstract

Owing to the quick spread of minute health-related experiences, the distillation of knowledge from such unstructured narratives is an extremely challenging task. In spite of the success of neural networks methods in improving learning structural reliability, they result in inadequate accuracy of the bio-medical sentiment classification when employing less useful features sets. Therefore, they lack discriminatory potential. In this study, we propose to add a deep associative memory into neural networks for an effective sentiment decomposition, which emphasizes correctly on bio-medical entities related to the extraction of different data-object properties, and contextual-semantics dependencies for a given aspect. The underlying trust of these measures is behind the ability to compute the completion of unseen medical patterns, where comprehensive bio-medical distributed representations are used for representing the formal medical connections from PubMed databases. Experiments on a biomedical sentiment analysis task show that the model provides comprehensive embeddings with meaningful medical patterns. It achieved an average performance of 87% on varied large online datasets. It also outperforms baselines in discovering and identifying medical natural concepts. We provide meaningful support to bio-medical sentiment analysis applications in social networks. Indeed, the facets of this study might be used in many health concerns such as analyzing change in health status or unexpected situations.

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Notes

  1. https://www.mayoclinic.org/diseases-conditions/epilepsy/symptoms-causes

  2. https://www.epilepsy.com/learn/early-death-and-sudep/sudep/

  3. https://pubmed.ncbi.nlm.nih.gov/

  4. https://www.primarilynlm.nih.gov/bsd/pmresources.html

  5. http://meetings.aps.org/link/BAPS.2017.MAR.P5.9

  6. http://SentiWordNet.isti.cnr.it/

  7. https://sentic.net/

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Authors and Affiliations

  1. LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Hanane Grissette & El Habib Nfaoui

Authors
  1. Hanane Grissette
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  2. El Habib Nfaoui
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Correspondence to Hanane Grissette.

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Grissette, H., Nfaoui, E. Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients’ narratives. Pers Ubiquit Comput 27, 2055–2069 (2023). https://doi.org/10.1007/s00779-021-01595-4

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