Acknowledgement
Research for this paper was carried out under the KICT Research Program(20230068-001, Research on the establishment of integrated and linked infrastructure for the co-prosperity of South and North Korea) funded by the Ministry of Science and ICT.
References
- An, J. and Kim, H. W. (2015), "Building a Korean Sentiment Lexicon Using Collective Intelligence", Journal of Intelligence and Information Systems, Vol.21, No.2, pp.49-67. https://doi.org/10.13088/jiis.2015.21.2.49
- Bigkinds (2023), http://www.bigkinds.or.kr.
- Cheong, Y., Wang, G. and Song, S. (2020), "A Deep Learning-based Analysis of Ideological Words in Rodong Sinmun", Korean Linguistics, Vol.88, pp.213-245. https://doi.org/10.20405/kl.2020.08.88.213
- Choi, G. and Choi, S. P. (2018), "A Study on the Deduction of Social Issues Applying Word Embedding: With an Empasis on News Articles related to the Disables", Journal of the Korean Society for Information Management, Vol.35, No.1, pp.231-250.
- Choi, Y. and Choi, S. P. (2019), "A Study on Patent Literature Classification Using Distributed Representation of Technical Terms", Journal of the Korean Society for Library and Information Science, Vol.53, No.2, pp.179-199.
- Chung, S., Moon, S. and Choi, S. (2018), "Bridge Damage Factor Recognition from Inspection Reports Usin Deep Learning", Journal of the Korean Society of Civil Engineers, Vol.38, No.4, pp.621-625. https://doi.org/10.12652/KSCE.2018.38.4.0621
- Harris, Z. S. (1954), "Distributional Structure", WORD, Vol.10, No.2-3, pp.146-162. https://doi.org/10.1080/00437956.1954.11659520
- Kim, K., Kang, K., Son, M., Lee, C., Hong, S. and Kim, S. (2020), "A Big-Data Analysis of Issues on North Korea and Media Agenda Setting Functions: Applying Topic Modeling and Word-embedding Methods", Peace and Democracy Institute, Vol.28, No.1, pp.287-33.
- Kim, K. O. (2020), "Analysis of Research Trends in Consumer Science through Text Mining", Journal of Consumer Studies, Vol.31, No.5, pp.19-47. https://doi.org/10.35736/JCS.31.5.2
- Kim, N. and Kim, H. J. (2017), "A Study on the Law2Vec Model for Searching Related Law", Journal of Digital Contents Society, Vol.18, No.7, pp.1419-1425.
- Park, K. (2023), Pre-trained word vectors of 30+ languages, https://github.com/Kyubyong/wordvectors.
- Rong, X. (2014), Word2vec parameter learning explained, Computation and Language(cs.CL).
- Song, J. and Lee, J. K. (2018), "Approach to Word Embedding-based Semantic Analysis of Building Rule Checking-related Sentences for the Automated Rule Checking", Korean Journal of Computational Design and Engineering, Vol.23, No.4, pp.384-393. https://doi.org/10.7315/CDE.2018.384
- Yang, Y. J., Lee, B. H., Kim, J. S., and Lee, K. Y. (2019), "Development of An Automatic Classification System for Game Reviews Based on Word Embedding and Vector Similarity", The Journal of Society for e-Business Studies, Vol.24, No.2, pp.1-14. https://doi.org/10.7838/JSEBS.2019.24.2.001
- Yoo, S. H. and Sung, S. (2021), "Methodology for Semantic R&D Knowledge Clustering Analysis through Data Similarity Analysis: Entrepreneurship Research Field Study", Journal of Business Research, Vol.36, No.3, pp.167-180.
- Yoo, W. and An, S. (2023), WikiDocs, https://wikidocs.net/book/2155.