Patent Keyword Analysis Using Bayesian Zero-Inflated Model and Text Mining

Abstract

Patent keyword analysis is used to analyze the technology keywords extracted from collected patent documents for specific technological fields. Thus, various methods related to this type of analysis have been researched in the industrial engineering fields, such as technology management and new product development. To analyze the patent document data, we have to search for patents related to the target technology and preprocess them to construct the patent–keyword matrix for statistical and machine learning algorithms. In general, a patent–keyword matrix has an extreme zero-inflated problem. This is because each keyword occupies one column even if it is included in only one document among all patent documents. General zero-inflated models have a limit at which the performance of the model deteriorates when the proportion of zeros becomes extremely large. To solve this problem, we applied a Bayesian inference to a general zero-inflated model. In this paper, we propose a patent keyword analysis using a Bayesian zero-inflated model to overcome the extreme zero-inflated problem in the patent–keyword matrix. In our experiments, we collected practical patents related to digital therapeutics technology and used the patent–keyword matrix preprocessed from them. We compared the performance of our proposed method with other comparative methods. Finally, we showed the validity and improved performance of our patent keyword analysis. We expect that our research can contribute to solving the extreme zero-inflated problem that occurs not only in patent keyword analysis, but also in various text big data analyses.