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Time gap analysis by the topic model-based temporal technique

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  • Jeong, Do-Heon
  • Song, Min

Abstract

This study proposes a temporal analysis method to utilize heterogeneous resources such as papers, patents, and web news articles in an integrated manner. We analyzed the time gap phenomena between three resources and two academic areas by conducting text mining-based content analysis. To this end, a topic modeling technique, Latent Dirichlet Allocation (LDA) was used to estimate the optimal time gaps among three resources (papers, patents, and web news articles) in two research domains. The contributions of this study are summarized as follows: firstly, we propose a new temporal analysis method to understand the content characteristics and trends of heterogeneous multiple resources in an integrated manner. We applied it to measure the exact time intervals between academic areas by understanding the time gap phenomena. The results of temporal analysis showed that the resources of the medical field had more up-to-date property than those of the computer field, and thus prompter disclosure to the public. Secondly, we adopted a power-law exponent measurement and content analysis to evaluate the proposed method. With the proposed method, we demonstrate how to analyze heterogeneous resources more precisely and comprehensively.

Suggested Citation

  • Jeong, Do-Heon & Song, Min, 2014. "Time gap analysis by the topic model-based temporal technique," Journal of Informetrics, Elsevier, vol. 8(3), pages 776-790.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:3:p:776-790
    DOI: 10.1016/j.joi.2014.07.005
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    Cited by:

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    3. Huailan Liu & Zhiwang Chen & Jie Tang & Yuan Zhou & Sheng Liu, 2020. "Mapping the technology evolution path: a novel model for dynamic topic detection and tracking," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2043-2090, December.
    4. Hong Wu & Huifang Yi & Chang Li, 2021. "An integrated approach for detecting and quantifying the topic evolutions of patent technology: a case study on graphene field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6301-6321, August.
    5. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).
    6. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    7. Munan Li, 2015. "A novel three-dimension perspective to explore technology evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1679-1697, December.
    8. Qiang Gao & Xiao Huang & Ke Dong & Zhentao Liang & Jiang Wu, 2022. "Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1543-1563, March.
    9. Qian, Yue & Liu, Yu & Sheng, Quan Z., 2020. "Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence," Journal of Informetrics, Elsevier, vol. 14(3).
    10. Wencan Tian & Yongzhen Wang & Zhigang Hu & Ruonan Cai & Guangyao Zhang & Xianwen Wang, 2024. "Does Granger causality exist between article usage and publication counts? A topic-level time-series evidence from IEEE Xplore," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3285-3302, June.

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