Objective:

To identify in an unsupervised manner through topic modeling the topics of greatest interest in the field of Occupational Health and Home Care Services from the scientific articles published on the subject.

Method:

The study used the unsupervised Machine Learning algorithm Dirichlet Latent Assignment for topic modeling and the NRC lexicon to carry out the sentiment analysis of the corpus of document files obtained from MEDLINE (via PubMed) using the descriptors “Occupational Health” and “Home Care Services”.

Results:

Of the total of 70 documentary files analyzed, it was obtained that the intensity of the emotions in the texts was low (ranging in values from 5 to 10), with positive feelings having a greater representation compared to negative ones in a ratio of 60/ 40. There was no variation in the proportions of emotions with respect to the study period. The four topics of greatest interest were identified in the articles analyzed: home care and caregiver satisfaction, breastfeeding period, rehabilitation programs, and physical activity to mitigate pain.

Conclusions:

It has been confirmed that natural language processing methodologies can be a great support tool for the analysis of scientific articles. Specifically, it has been possible to determine in a clear and unsupervised manner the topics of greatest interest in the field of Occupational Health and Home Care Services.

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