Using Manual and Computer-Based Text-Mining to Uncover Research Trends for <i>Apis mellifera</i>

• Main Authors: Esmaeil Amiri, Prashant Waiker, Olav Rueppell, Prashanti Manda
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Veterinary Sciences
• Subjects: text-mining; topic modeling; colony collapse disorder; genomics; <i>Varroa</i> mite; honey bee health
• Online Access: https://www.mdpi.com/2306-7381/7/2/61

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 <i>Varroa</i> 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.