利用动态主题建模检测研究趋势

2022 7th International Conference on Data Science and Machine Learning Applications (CDMA) Pub Date : 2022-03-01 DOI:10.1109/CDMA54072.2022.00031

Amal Alazba, Leina Abouhagar, Randah Al-Harbi, Hamdi A. Al-Jamimi, Abdullah Sultan, Rabah A. Al-Zaidy

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引用次数: 0

摘要

发现研究领域的趋势有助于研究人员发现一个领域或研究领域的最新进展。此外，大学的政策制定者可以利用这些信息进行决策。不同的因素对研究课题的成长和演变有着直接的影响。这些因素包括资金、社区利益和国家需求。在本文中，我们提出了一种无监督动态主题建模方法，使用来自相应研究领域的出版物集合来发现和分析一组研究领域中最热门的研究主题。此外，我们还研究了新兴研究趋势与不同影响因素之间的相关性。

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Detection of Research Trends using Dynamic Topic Modeling

Discovering trends in research areas is helpful for researchers in finding the recent advances in a field or area of research. In addition, policy makers in universities can utilize this information in decision making. Different factors have direct influence on the growth and evolution of research topics. These include the funding, community interest and national needs. In this paper, we propose an unsupervised Dynamic Topic Modeling approach to discover and analyze the most trending research topics in a set of research areas using a collection of publications from the corresponding research areas. Furthermore, we study the correlation between emerging research trends and the different influencing factors.

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2022 7th International Conference on Data Science and Machine Learning Applications (CDMA)

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