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An alternative topic model based on Common Interest Authors for topic evolution analysis

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  • Jung, Sukhwan
  • Yoon, Wan Chul
Abstract
Topic modeling methods aim to extract semantic topics from unstructured documents, and topic evolution is one of its branches seeking to analyze how temporal topics in a set of documents evolve and has shown successful identification of content transitions within static topics over time; yet, the inherent limitations of topic modeling methods inhibit traditional topic evolution methods from highlighting topical correlations between different, dynamic topics. The authors propose an alternative topic modeling method conscious of the topical correlation in the academic domain by introducing the notion of the common interest authors (CIA11CIA: Common Interest Authors), defining a topic as a set of shared common research interests of a researcher group. Publication records related to the Human Computer Interaction field were extracted from the Microsoft Academic Graph dataset, with virtual reality as the target field of research. The result showed that the proposed alternative topic modeling is capable of successfully model coherent topics regardless of the topic size with only the meta-data of the document set, indicating that the alternative approach is not only capable of allowing topic correlation analysis during the topic evolution but also able to generate coherent topics at the same time.

Suggested Citation

  • Jung, Sukhwan & Yoon, Wan Chul, 2020. "An alternative topic model based on Common Interest Authors for topic evolution analysis," Journal of Informetrics, Elsevier, vol. 14(3).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:3:s1751157719303517
    DOI: 10.1016/j.joi.2020.101040
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    References listed on IDEAS

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    3. Weibin Lin & Xianli Wu & Zhengwei Wang & Xiaoji Wan & Hailin Li, 2022. "Topic Network Analysis Based on Co-Occurrence Time Series Clustering," Mathematics, MDPI, vol. 10(16), pages 1-17, August.
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    6. Seyyed Reza Taher Harikandeh & Sadegh Aliakbary & Soroush Taheri, 2023. "An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1567-1582, March.
    7. Doo-San Kim & Byeong-Cheol Lee & Kwang-Hi Park, 2021. "Determination of Motivating Factors of Urban Forest Visitors through Latent Dirichlet Allocation Topic Modeling," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    8. Tian, Yunpei & Li, Gang & Mao, Jin, 2023. "Predicting the evolution of scientific communities by interpretable machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
    9. Jung, Sukhwan & Segev, Aviv, 2022. "DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction," Journal of Informetrics, Elsevier, vol. 16(3).
    10. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    11. Hengmin Zhu & Li Qian & Wang Qin & Jing Wei & Chao Shen, 2022. "Evolution analysis of online topics based on ‘word-topic’ coupling network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3767-3792, July.

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