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论文类型：期刊论文

发表时间：2014-10-01

发表刊物：SCIENTOMETRICS

收录刊物：SCIE、SSCI

卷号：101

期号：1

页面范围：685-704

ISSN号：0138-9130

关键字：Noun phrases extraction; Topic model (LDA); Institution-topic model; Content analysis; Long term evolution (LTE)

摘要：An extended latent Dirichlet allocation (LDA) model is presented in this paper for patent competitive intelligence analysis. After part-of-speech tagging and defining the noun phrase extraction rules, technological words have been extracted from patent titles and abstracts. This allows us to go one step further and perform patent analysis at content level. Then LDA model is used for identifying underlying topic structures based on latent relationships of technological words extracted. This helped us to review research hot spots and directions in subclasses of patented technology in a certain field. For the extension of the traditional LDA model, another institution-topic probability level is added to the original LDA model. Direct competing enterprises' distribution probability and their technological positions are identified in each topic. Then a case study is carried on within one of the core patented technology in next generation telecommunication technology-LTE. This empirical study reveals emerging hot spots of LTE technology, and finds that major companies in this field have been focused on different technological fields with different competitive positions.