Tracing the evolution of physics with a keyword co-occurrence network

You, Taekho; Yoon, Jisung; Kwon, Oh-Hyun; Jung, Woo-Sung

doi:10.1007/s40042-020-00051-5

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…Keywords are the core natural language vocabulary to express the subject, content, ideas, and research methods of the literature (You et al 2021). Keywords represent the topics of the domain, and cluster analysis of these words can reflect the structure and association of topics.…”

Section: Methodsmentioning

confidence: 99%

Survey on sentiment analysis: evolution of research methods and topics

Cui

Wang

et al. 2023

Artif Intell Rev

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Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword cooccurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

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Section: Methodsmentioning

confidence: 99%

Survey on sentiment analysis: evolution of research methods and topics

Cui

Wang

et al. 2023

Artif Intell Rev

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“…Keyword cooccurrence networks, in particular, is an appropriate method to reveal knowledge structure in a set of scientific corpora. [11] For example, keyword co-occurrence networks are applied to study the knowledge structure of physics, technology, and patents [12][13][14]. Also, constructing a keyword co-occurrence network allows us to use various network analysis methods, such as community detection, centrality, PageRank, and other network properties.…”

Section: Introductionmentioning

confidence: 99%

Revealing role of Korean Physics Society with keyword co-occurrence network

Jo,

Park,

Yoon

et al. 2022

Preprint

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Science and society inevitably interact with each other and evolve together. Studying the trend of science helps recognize leading topics significant for research and establish better policies to allocate funds efficiently. Scholarly societies such as the Korean Physics Society (KPS) also play an important role in the history of science. Figuring out the role of these scholarly societies motivate our research related with our society since societies pay attention to improve our society. Although several studies try to capture the trend of science leveraging scientific documents such as paper or patents, but these studies limited their research scope only to the academic world, neglecting the interaction with society. Here we try to understand the trend of science along with society using a public magazine named "Physics and High Technology," published by the Korean Physics Society (KPS). We build keyword co-occurrence networks for each time period and applied community detection to capture the keyword structure and tracked the structure's evolution. In the networks, a research-related cluster is consistently dominant over time, and sub-clusters of the research-related cluster divide into various fields of physics, implying specialization of the physics discipline. Also, we found that education and policy clusters appear consistently, revealing the KPS's contribution to science and society. Furthermore, we applied PageRank algorithm to selected keywords ('semiconductor', 'woman', 'evading'...) to investigate the temporal change of the importance of keywords in the network. For example, the importance of the keyword 'woman' increases as time goes by, indicating that academia also pays attention to gender issues reflecting the social movement in recent years.

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“…At present, the frontier research and disciplinary development exploration of scientific research based on academic literature data has become quite an active field (Chen et al, 2016 ). Numerous studies have started from the experience of domain experts and use a priori knowledge as a guide to conduct research and discoveries in the scientific literature (You et al, 2021 ). We also note that several fascinating studies use various forms of keyword analysis such as co-occurrence network to mine the domain knowledge based on bibliometrics (Su and Lee, 2010 ; Dehdarirad et al, 2014 ; Radhakrishnan et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

TELS: Evolution patterns of research keywords from the evidence of PNAS Social Sciences topics

et al. 2022

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By reviewing scientific literature, researchers may obtain a comprehensive understanding of field developments, keeping abreast of the current research status and hotspot shifts. The evolution pattern of keywords is supposed to be an efficient indicator in revealing the shifting and sustainability configuration of scientific concepts, ideas, and research hotspots. Here we take an extensive investigation of the evolution of keywords among all publications in PNAS Social Sciences from 1990 to 2021. Statistical tests show the keyword mention time series always accompanied by the emergence of a log-normal distribution. Additionally, we introduce a novel schema of four patterns (TELS), which are Transient impact type, Explosive impact type, Large impact type, and Small impact type, respectively, to illustrate the evolution of keywords. The TELS schema can be used to capture the whole life circle feature of any proposed keyword, from a pool of candidates. By dividing the entire time into four periods, we also introduce the concept of elite keywords to reveal the temporal feature of social sciences focus. An explicit transition from anthropology research to neuroscience and social problems research can be observed from the evolution diagram. We argue that the proposed method is of general sense and might be applicable to other fields of science.

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Tracing the evolution of physics with a keyword co-occurrence network

Cited by 12 publications

References 14 publications

Survey on sentiment analysis: evolution of research methods and topics

Survey on sentiment analysis: evolution of research methods and topics

Revealing role of Korean Physics Society with keyword co-occurrence network

TELS: Evolution patterns of research keywords from the evidence of PNAS Social Sciences topics

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