Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures: An application in Physics

Abstract

Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These “exclusive journals” are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy‐makers, funding, and research institutions—via more accurate academic performance evaluations—, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories.

Mapping the Landscape of Artificial Intelligence in Supply Chain Management: A Bibliometric Analysis

“…They supplement the amount of knowledge about a subject by giving an independent expansion of author keywords. Both AK and KP are required for bibliometric analysis since they disclose commonly recurring phrases and concepts, enabling researchers in gaining a more holistic view of existing research efforts across multiple subfields (Zhang et al, 2016;Pech et al, 2022).…”

Research Progress, Hotspots, and Evolution of Nighttime Light Pollution: Analysis Based on WOS Database and Remote Sensing Data

“…Keywords plus are correlative keywords extracted from the titles of documents and their cited references, usually generated automatically by clustering algorithms, and not necessarily located in the title of the article or appearing as author keywords [70]. Many studies have shown that keywords plus are able to capture the content of the literature with greater depth and diversity than author keywords and are often employed as a key metric in the bibliometric analysis [71][72][73]. Keywords plus have also been widely applied to identify research trends and hotspots in many domains, such as climate change [74], air pollution [75], biomedicine [76], etc.…”