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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
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Data driven identification of international cutting edge science and technologies using SpaCy
Chunqi Hu1, Huaping Gong1, Yiqing He2
1School of Public Policy and Administration, Nanchang University, Nanchang, Jiangxi, China.
Plos One
|October 12, 2022
Summary
Identifying cutting-edge science and technology is crucial for innovation. This study presents a data-driven model using SpaCy and machine learning to effectively identify emerging technological trends from media data.
Area of Science:
- Scientific research and technological innovation
- Data science and artificial intelligence
Background:
- Research on cutting-edge science and technology is hindered by challenges in data collection, processing, and identification.
- An effective data-driven method is essential for promoting advanced technologies.
Purpose of the Study:
- To propose and validate a data-driven model for identifying global cutting-edge science and technology.
- To enhance the identification and tracking of emerging technological trends.
Main Methods:
- Web crawling using Python to collect data from 17 prominent US technology media websites (July 2019-July 2020).
- Implementation of a model combining graph-based neural network learning with active learning.
- Utilizing a ten-fold cross-validation approach for model training and validation through machine learning.
Main Results:
- The developed model demonstrated high performance in entity recognition tasks, achieving an F-score of 98.11%.
- The model successfully identified and tracked cutting-edge technologies from collected data.
Conclusions:
- The proposed data-driven model serves as a valuable information source for identifying cutting-edge technologies.
- This approach can foster innovation in advanced technologies and optimize scientific research workflows.
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