面向教育技术学文献数据的主题挖掘

Topic Mining Based on Educational Technology Literatures

对网络环境下海量的科技文献数据进行文本挖掘可以有效地提高科技文献信息的可用性，发现隐藏的知识。LDA（Latent Dirichlet Allocation）模型是一种能够提取文本隐含主题的非监督学习模型。论文基于LDA模型，以三种国际教育技术期刊在2004─2008年间出版的学术文献为研究对象，进行了主题挖掘和文献分析。

Text mining of massive scientific literatures in network environment can improve usability effectively and mine latent knowledge. Latent Dirichlet Allocation model is an unsupervised learning one which can extract latent topics in texts. Based on LDA， this paper conducted a study of topic mining and content analysis with the dataset of three educational technology periodicals between 204 and 2008.