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2024;11 1463332.

An overview of the literature on assistance dogs using text mining and topic analysis.

Emma Bassan, Alberto Mair, Marta De Santis, Massimo Bugianelli, Enrico Loretti, Alessio Capecci, Franco Mutinelli, Laura Contalbrigo
Author Information
  1. Emma Bassan: National Reference Centre for Animal Assisted Interventions, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy.
  2. Alberto Mair: National Reference Centre for Animal Assisted Interventions, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy.
  3. Marta De Santis: National Reference Centre for Animal Assisted Interventions, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy.
  4. Massimo Bugianelli: National Guide Dog School for the Blind, Scandicci, Italy.
  5. Enrico Loretti: Local Healthcare Unit "Toscana Centro", Firenze, Italy.
  6. Alessio Capecci: Health, Welfare and Social Affairs Direction Regione Toscana, Firenze, Italy.
  7. Franco Mutinelli: National Reference Centre for Animal Assisted Interventions, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy.
  8. Laura Contalbrigo: National Reference Centre for Animal Assisted Interventions, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy.
PMID: 39723180 DOI: 10.3389/fvets.2024.1463332

Abstract

It is said that dogs are human's best friend. On occasion, dogs can be raised and trained to provide additional specific benefits to humans suffering from a range of physical or mental conditions, working as assistance dogs. In this article, we employed innovative techniques to review the vast and constantly expanding literature on the subject, which covers a multitude of aspects. The 450 articles obtained through keyword search on Scopus were initially described in terms of year of publication, geographical context and publication destination, and were subsequently analysed through automated text mining to detect the most important words contained within them. Lastly, a generative model of topic analysis (Latent Dirichlet Allocation-LDA) described the content of the collection of documents, dividing it into the appropriate number of topics. The results yielded interesting insights across all domains, demonstrating the potential of automated text mining and topic analysis as a useful tool to support the researchers in dealing with complex and time-consuming subjects' reviews, integrating the work done with traditional reviewing methods.

Keywords

Latent Dirichlet Allocation assistance dogs generative model service dogs text mining topic analysis

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Journal Article Review
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