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Network-based topic structure visualization.

Yeseul Jeon^1,2, Jina Park^1,2, Ick Hoon Jin^1,2

¹Department of Statistics and Data Science, Yonsei University, Seoul, South Korea.

Journal of Applied Statistics

|February 10, 2025

Summary

This summary is machine-generated.

This study models topic structures using latent space item response models and word distributions. It reveals topic relationships and maturity, demonstrated with COVID-19 research, offering insights for scientific discovery.

Keywords:

Latent space item response model network analysis text mining topic embedding topic structure visualization

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Area of Science:

Computational Linguistics
Bibliometrics
Network Science

Background:

Inter-correlated topics pose challenges for research structure analysis.
Understanding topic relationships is crucial for guiding research direction and insights.

Purpose of the Study:

To model and visualize the structure and relationships between topics.
To develop a method for assessing topic maturity and robustness.
To apply the approach to analyze the topic composition of COVID-19 studies.

Main Methods:

Utilizing topic-word distributions from topic models as item-response data.
Employing a latent space item response model to estimate latent topic positions.
Developing a novel scoring scheme for topic characterization and representative word selection.

Main Results:

Successfully modeled topic structures and visualized latent topic positions in Euclidean space.
Identified topic proximity and associations, revealing underlying relationships.
Assessed topic maturity by tracking latent positions across different word sets.

Conclusions:

The latent space item response model effectively captures and visualizes topic structures and relationships.
The approach provides valuable insights into topic interplays and research evolution, as shown in the COVID-19 literature analysis.
The developed methods offer a robust framework for analyzing complex research landscapes.