{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T12:37:36Z","timestamp":1763987856619,"version":"3.41.2"},"reference-count":45,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2018,2,19]],"date-time":"2018-02-19T00:00:00Z","timestamp":1518998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["LHT"],"published-print":{"date-parts":[[2018,6,4]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind mainly depends on human justification. The purpose of this paper is to demonstrate how to identify research topics and evolution in trends from library electronic references efficiently and effectively by employing automatic text analysis algorithms.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The authors used the latent Dirichlet allocation (LDA), a probabilistic generative topic model to extract the latent topic from the large quantity of research abstracts. Then, the authors conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>First, this paper discovers 32 significant research topics from the abstracts of 3,737 articles published in the six top accounting journals during the period of 1992-2014. Second, based on the document-topic distributions generated by LDA, the authors identified seven hot topics and six cold topics from the 32 topics.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The topics discovered by LDA are highly consistent with the topics identified by human experts, indicating the validity and effectiveness of the methodology. Therefore, this paper provides novel knowledge to the accounting literature and demonstrates a methodology and process for topic discovery with lower cost and higher efficiency than the current methods.<\/jats:p><\/jats:sec>","DOI":"10.1108\/lht-06-2017-0132","type":"journal-article","created":{"date-parts":[[2018,2,19]],"date-time":"2018-02-19T09:45:02Z","timestamp":1519033502000},"page":"400-410","source":"Crossref","is-referenced-by-count":22,"title":["Discovering research topics from library electronic references using latent Dirichlet allocation"],"prefix":"10.1108","volume":"36","author":[{"given":"Debin","family":"Fang","sequence":"first","affiliation":[]},{"given":"Haixia","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Baojun","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Xiaojun","family":"Li","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2018,2,19]]},"reference":[{"issue":"1","key":"key2021041509200373300_ref001","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s11263-007-0072-x","article-title":"Multilevel image coding with hyperfeatures","volume":"78","year":"2008","journal-title":"International Journal of Computer Vision"},{"issue":"1","key":"key2021041509200373300_ref002","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s11192-011-0374-1","article-title":"Co-word analysis of the trends in stem cells field based on subject heading weighting","volume":"88","year":"2011","journal-title":"Scientometrics"},{"issue":"6","key":"key2021041509200373300_ref003","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1287\/mnsc.2014.1930","article-title":"Simultaneously discovering and quantifying risk types from textual risk disclosures","volume":"60","year":"2014","journal-title":"Management Science"},{"issue":"4","key":"key2021041509200373300_ref004","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1145\/2133806.2133826","article-title":"Probabilistic topic models","volume":"55","year":"2012","journal-title":"Communications of the ACM"},{"first-page":"113","article-title":"Dynamic topic models","year":"2006","key":"key2021041509200373300_ref005"},{"issue":"1","key":"key2021041509200373300_ref006","first-page":"17","article-title":"Correction: a correlated topic model of science","volume":"1","year":"2007","journal-title":"The Annals of Applied Statistics"},{"key":"key2021041509200373300_ref007","first-page":"993","article-title":"Latent Dirichlet allocation","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"key2021041509200373300_ref009","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/BF02016546","article-title":"Historical scientometrics? 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