Due to the introduction of text mining, studies have been conducted to analyze meaningful topics and trends in document collections. Trend analysis using Latent Dirichlet Allocation (LDA) in text mining is adopted as one of the trend analysis methods with high accuracy. In this paper, we propose a trend analysis method using LDA. The method is composed of 5 steps and the trend analysis is performed by topic using the extracted result combining LDA and Top 10 keywords. By applying our method and LDA to international standards documents, we performed topic modeling and checked the trend of international standards using extracted topics.

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Recently international organizations such as ISO/TC211, OGC, INSPIRE (Infrastructure for Spatial Information in Europe) make an effort to share geospatial data using semantic web technologies. In addition, smart phone and social networking services enable community-based opportunities for participants to share issues of a social phenomenon based on geographic area, and many researchers try to find a method of extracting issues from that. However, serviceable spatial ontologies are still insufficient at application level, and studies of spatial information extraction from SNS were focused on user's location finding or geocoding by text mining. Therefore, a study of extracting spatial phenomenon from social media information and converting it into geosemantic knowledge is very usable. In this paper, we propose a framework for extracting keywords from micro-blog, one of the social media services, finding their relationships using data mining technique, and converting it into spatiotemopral knowledge. The result of this study could be used for implementing a related system as a procedure and ontology model for constructing geoseemantic issue. And from this, it is expected to improve the effectiveness of finding, publishing and analysing spatial issues.

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Text mining has been introduced to analyze meaningful topics and trends in documents, and researches has been conducted in various fields. Since the international standard documents are written in relation to each subject, it becomes data for applying text mining. In this study, topic modeling using cloud system has conducted based on 471 documents belonging to the ITU-T X series, and representative topics are extracted by periods. Topic modeling has applied to 3,975 documents, and the trend analysis function was performed by using the occurrence rate extracted from each document. The result of this study would contribute to the invigoration of research in the field of international standards and security.