Technological topic analysis of standard-essential patents based on the improved Latent Dirichlet Allocation (LDA) model

Abstract

ABSTRACT Standard-essential patents (SEPs) are an important technological resource for firms in the telecommunication industry. The utilisation of technological topic analysis to reveal the global development dynamics of SEPs has significant theoretical and practical implications. First, this study defines the phrase extraction rules and constructs a phrase importance evaluation model to extract key technical phrases in the patent text. Second, the extracted key phrases are used as input for the Latent Dirichlet Allocation (LDA) model, and the relative independence (RI) model is proposed to determine the optimal number of topics based on two dimensions of coherence and similarity. Finally, the technological topic analysis based on the improved LDA model is performed on 30,154 texts of declared 5G SEPs. The results show that (1) the RI model can better identify the optimal number of topics for the LDA model; (2) 23 key technologies and four hot spots in 5G are identified based on the improved LDA model; (3) different firms have different technological layouts, and the diversification trend of technology development appears; and (4) the forecasting results also reveal the dynamics of emerging and declining technical areas in the 5G industry.