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Article

Using Manual and Computer-Based Text-Mining to Uncover Research Trends for Apis mellifera

1
Department of Biology, University of North Carolina at Greensboro, Greensboro, NC 27402, USA
2
Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC 27402, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Vet. Sci. 2020, 7(2), 61; https://doi.org/10.3390/vetsci7020061
Received: 28 March 2020 / Revised: 30 April 2020 / Accepted: 2 May 2020 / Published: 6 May 2020
(This article belongs to the Special Issue Honey Bee Health)
Honey bee research is believed to be influenced dramatically by colony collapse disorder (CCD) and the sequenced genome release in 2006, but this assertion has never been tested. By employing text-mining approaches, research trends were tested by analyzing over 14,000 publications during the period of 1957 to 2017. Quantitatively, the data revealed an exponential growth until 2010 when the number of articles published per year ceased following the trend. Analysis of author-assigned keywords revealed that changes in keywords occurred roughly every decade with the most fundamental change in 1991–1992, instead of 2006. This change might be due to several factors including the research intensification on the Varroa mite. The genome release and CCD had quantitively only minor effects, mainly on honey bee health-related topics post-2006. Further analysis revealed that computational topic modeling can provide potentially hidden information and connections between some topics that might be ignored in author-assigned keywords. View Full-Text
Keywords: text-mining; topic modeling; colony collapse disorder; genomics; Varroa mite; honey bee health; Apis mellifera text-mining; topic modeling; colony collapse disorder; genomics; Varroa mite; honey bee health; Apis mellifera
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MDPI and ACS Style

Amiri, E.; Waiker, P.; Rueppell, O.; Manda, P. Using Manual and Computer-Based Text-Mining to Uncover Research Trends for Apis mellifera. Vet. Sci. 2020, 7, 61. https://doi.org/10.3390/vetsci7020061

AMA Style

Amiri E, Waiker P, Rueppell O, Manda P. Using Manual and Computer-Based Text-Mining to Uncover Research Trends for Apis mellifera. Veterinary Sciences. 2020; 7(2):61. https://doi.org/10.3390/vetsci7020061

Chicago/Turabian Style

Amiri, Esmaeil, Prashant Waiker, Olav Rueppell, and Prashanti Manda. 2020. "Using Manual and Computer-Based Text-Mining to Uncover Research Trends for Apis mellifera" Veterinary Sciences 7, no. 2: 61. https://doi.org/10.3390/vetsci7020061

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