AUTHORS: Ju Seop Park, Na Rang Kim, Hyung-Rim Choi, Eunjung Han

Download as PDF

ABSTRACT: Although the Delphi technique is often used to forecast promising future technologies, this method is difficult, time-consuming, and costly. As an alternative, the Latent Dirichlet Allocation (LDA) topic modeling technique can be used. Therefore, this study aimed to develop a science and technology trend forecasting system using the LDA topic modeling technique as a form of text mining. An empirical analysis of 13,618 abstracts regarding U.S. artificial intelligence (AI)-related patents was conducted, and the results of the analysis were verified based on changes in the frequency of related words within the AI topics. The trend analysis of the AI topics resulted in six hot technologies and six cold technologies. The results of the verification showed that 8 out of the 11 technologies matched (1 technology could not be verified). This study provides a foundation for engine design by helping develop engines that enable simple and low-cost technology forecasting and by suggesting an appropriate topic modeling technique. The study also makes an academic contribution by encouraging follow-up studies. Moreover, the developed forecasting system may be used as an automated forecasting engine to conduct tasks related to regional innovation

KEYWORDS: Development of prediction systems, scientific technology trends, technological prediction, text mining, topic modeling, analysis of technological trends

REFERENCES:

[1] Cho, B. S., Ji, K. Y., Kim, Y. J. and Lee, B. G., Future Technology Forecast Process, ETRI Planning Report, 2009.

[2] Park, J. H. and Song, M., A Study on the Research Trends in Library & Information Science in Korea using Topic Modeling, Korea Society for Information Management, Vol. 30, No. 1, 2013, pp. 7-32.

[3] Jern, S. H., Park, S. S. and Jang, D. S., Patent Analysis and Technology Forecasting, Kyowoosa, 2014.

[4] Ahn, D. H., Shin, T. Y., Mun, M. J. and Kim, H. S., Future Socio-Economic Issues and Needs for Technology Foresight, Science and Technology Policy Insititute, Report, 2003.

[5] Lee, S. K., Kim, S. I., Choi, C. T., Ahn, J. H., You, J. W. and Jerng, S. H., A Study on the Selection of the 10 Most Promising Technologies of KISTEP in 2016, KISTEP Research Report 2016- 080, 2016.

[6] Jeong, S. Y., Nam, S. I., Hong, S. and Han, C. H., Future Technology Foresight for an Enterprise : Methodology and Case, The Journal of Society for e-Business Studies, Vol. 11, No. 1, 2006, pp. 69-82.

[7] Blei, D. M., Ng, A. Y. and Jordan, M. I., Latent Dirichlet Allocation, The Journal of Machine Learning Research, Vol. 3, 2003, pp. 993-1022.

[8] Kim, J. H., “Research Trends Analysis for ‘Internet of Things’Based on Topic Modeling and Network Analysis”, Seoul National University of Science and Technology Master's Thesis, 2016.

[9] Kim, S. K., and Jang, S. Y., “A Study on the Research Trends in Domestic Industrial and Management Engineer”, Journal of the Korea Management Engineers Society, Vol. 21, No. 3, pp. 71-95, 2016.

[10] Kim, M. A., and Suh, C. K., “SCM Patent Analysis Using Topic Modeling: 1997~2016”, Journal of the Korean Society of Supply Chain Management, Vol. 17, No. 2, pp. 19-29, 2017.

[11] Jeong, B. K., and Lee, H. Y., “Research Topics in Industrial Engineering 2001~2015”, Journal of the Korean Institute of Industrial Engineers, Vol. 42, No. 6, pp. 421-431, 2016.

[12] Noh, B. J., Suh, J. S., Lee, J. U., Park, D. H., and Chung, Y. H., “Keyword Network Based Repercussion Effect Analysis of Foot-and-Mouth Disease Using Online News”, Journal of Korean Institute of Information Technology, Vol. 14, No. 9, pp. 143-152, 2016.

[13] Lee, S. Y., and Lee, K. M., “Trend Extraction using Topic Model Based on Reply Graph”, Proceedings of the Conference of the Korean Institute of Intelligent Systems, Vol. 24, No. 2, pp. 99-100, 2014.

[14] Song, M. and Kim, S. Y., Detecting the Knowledge Structure of Bioinformatics by Mining Full-text Collections, Scientometrics, Vol. 96, No. 1, 2013, pp. 183-201.

[15] Ko, B. Y. and Lo, H. S., Discovery of Promising Business Items by Technology-industry Concordance and Keyword Co-occurrence Analysis of US Patents, Journal of Korea Technology Innovation Society, Vol. 8, No. 2, 2005, pp. 860- 885.