Using sentiment analysis to identify similarities and differences in research topics and medical subject headings (MeSH terms) between Medicine (Baltimore) and the Journal of the Formosan Medical Association (JFMA) in 2020

Abstract

Background: Little systematic information has been collected about the nature and types of articles published in 2 journals by identifying the latent topics and analyzing the extracted research themes and sentiments using text mining and machine learning within the 2020 time frame. The goals of this study were to conduct a content analysis of articles published in 2 journals, describe the research type, identify possible gaps, and propose future agendas for readers.Methods: We downloaded 5610 abstracts in the journals of Medicine (Baltimore) and the Journal of the Formosan Medical Association (JFMA) from the PubMed library in 2020. Sentiment analysis (ie, opinion mining using a natural language processing technique) was performed to determine whether the article abstract was positive or negative toward sentiment to help readers capture article characteristics from journals. Cluster analysis was used to identify article topics based on medical subject headings (MeSH terms) using social network analysis (SNA). Forest plots were applied to distinguish the similarities and differences in article mood and MeSH terms between these 2 journals. The Q statistic and I 2 index were used to evaluate the difference in proportions of MeSH terms in journals. Results:The comparison of research topics between the 2 journals using the 737 cited articles was made and found that most authors are from mainland China and Taiwan in Medicine and JFMA, respectively, similarity is supported by observing the abstract mood (Q = 8.3, I 2 = 0, P = .68; Z = 0.46, P = .65), 2 journals are in a common cluster (named latent topic of patient and treatment) using SNA, and difference in overall effect was found by the odds ratios of MeSH terms (Q = 185.5 I 2 = 89.8, P< .001; Z = 5.93, P < .001) and a greater proportion of COVID-19 articles in JFMA.Conclusions: SNA and forest plots were provided to readers with deep insight into the relationships between journals in research topics using MeSH terms. The results of this research provide readers with a concept diagram for future submissions to a given journal.Abbreviations: AAC = absolute advantage coefficient, MeH = medical subject headings, OR = odds ratio, SNA = social network analysis.

The use of radar plots with the Yk-index to identify which authors contributed the most to the journal of Medicine in 2020 and 2021: A bibliometric analysis

“…China has an ACC of 0.74 (>0.70), which indicates a significant advantage over the next 2 countries. According to a previous study, [ 52 ] citations were used to compute the ACC (0.85) for China, which has a strong dominance in Medicine (Baltimore ).…”

“…The absolute advantage coefficient) [ 51 , 52 ] based on j or k in Equations (1), (2), and (5) was used to measure the strength of contributions compared to the next 2 big AIC/R.…”

“…The cutting point is set at 0.7. [ 51 , 52 ] Higher values indicate a greater role of dominance within the group. Three effect sizes are assigned: large (>=0.7), medium (between 0.5 and 0.7), and small (<0.5).…”

Using the forest plot to compare citation achievements in bibliographic and meta-analysis studies since 2011 using data on PubMed Central: A retrospective study

“…Online forest plot allows readers to examine the information of their own interest on the dashboard that could be useful with the zoom-in and zoom-out function superior to the traditionally statistic graphics as presented in the previous studies. [35][36][37]…”

The Hirsch-index in self-citation rates with articles in Medicine (Baltimore): Bibliometric analysis of publications in two stages from 2018 to 2021

“…[ 10 ] Meanwhile, Clarivate reports that self-citation in the Web of Science ranges from 0% to 15%, [ 11 ] and particularly in management journals, self-citation is much lower than 10%. [ 12 ] The SCR of articles published in the journal Medicine (Baltimore ), which ranked in the top 10 journals with Taiwan authors in 2020, [ 13 ] motivated us to examine it.…”