Title :
Text topic mining based on LDA and co-occurrence theory
Author :
Maowen, Wu ; Zhang Cai Dong ; Weiyao, Lan ; Wu Qing Qiang
Author_Institution :
Sch. of Journalism & Commun., Xiamen Univ., Xiamen, China
Abstract :
Based on the introduction to research background of stem cell and the significance of topic analysis of stem cell, this paper analyzed the topic analysis in co-occurrence theory and LDA. LDA and co-occurrence theory were used to determine the text topics of stem cell research literatures from 2006-2011 in PubMed. After stem cell research topics were obtained, they were analyzed in terms of topic label, topic research content and interrelation between topics. In the end, current deficiencies of LDA and future study are proposed.
Keywords :
biology computing; data mining; statistical analysis; text analysis; LDA; PubMed; co-occurrence theory; latent Dirichlet allocation; stem cell research topics; text topic mining; topic analysis; Algorithm design and analysis; Couplings; Data mining; Educational institutions; Indexes; Neoplasms; Stem cells; Latent Dirichlet Allocation (LDA); co-occurrence theory; correlation analysis;
Conference_Titel :
Computer Science & Education (ICCSE), 2012 7th International Conference on
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4673-0241-8
DOI :
10.1109/ICCSE.2012.6295129
