Amal Alazba, Leina Abouhagar, Randah Al-Harbi, Hamdi A. Al-Jamimi, Abdullah Sultan, Rabah A. Al-Zaidy
{"title":"Detection of Research Trends using Dynamic Topic Modeling","authors":"Amal Alazba, Leina Abouhagar, Randah Al-Harbi, Hamdi A. Al-Jamimi, Abdullah Sultan, Rabah A. Al-Zaidy","doi":"10.1109/CDMA54072.2022.00031","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":313042,"journal":{"name":"2022 7th International Conference on Data Science and Machine Learning Applications (CDMA)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Data Science and Machine Learning Applications (CDMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDMA54072.2022.00031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
